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How do I make append work as intended with arrays?


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3















I'm having problem with this code. I think I'm doing something wrong.



 import numpy as np

array = np.zeros(10)

arrays = []

for i in range(len(array)):
array[i] = 1
arrays.append(array)

print(arrays[0])


I was expecting to get:[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
But I'm getting:[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]



That is the last array I appended to arrays, and not the first one. Why is that happening and more important what can I do to get the desired output?










share|improve this question



















  • 3





    Hint: how many different array are there in your code? How many arrays do you create?

    – Jörg W Mittag
    Mar 25 at 4:56






  • 1





    Also why not using np.eye?

    – Julien
    Mar 25 at 4:58











  • convert array to nympy array

    – prashant rana
    Mar 25 at 4:58











  • I used a simple example to show what's happening, in reality I'm using array of matrices with values I get from data, but the problem is the same.

    – Alejandro Ruiz
    Mar 25 at 5:02






  • 1





    When you modify an object - list, dictionary, ndarray, and append it to a list, you need to append a copy, not the object that you keep modifying. Otherwise, all elements of the list will end up looking the same - because they are the same object. List append does not automatically save a copy; you have to do that yourself.

    – hpaulj
    Mar 25 at 6:59

















3















I'm having problem with this code. I think I'm doing something wrong.



 import numpy as np

array = np.zeros(10)

arrays = []

for i in range(len(array)):
array[i] = 1
arrays.append(array)

print(arrays[0])


I was expecting to get:[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
But I'm getting:[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]



That is the last array I appended to arrays, and not the first one. Why is that happening and more important what can I do to get the desired output?










share|improve this question



















  • 3





    Hint: how many different array are there in your code? How many arrays do you create?

    – Jörg W Mittag
    Mar 25 at 4:56






  • 1





    Also why not using np.eye?

    – Julien
    Mar 25 at 4:58











  • convert array to nympy array

    – prashant rana
    Mar 25 at 4:58











  • I used a simple example to show what's happening, in reality I'm using array of matrices with values I get from data, but the problem is the same.

    – Alejandro Ruiz
    Mar 25 at 5:02






  • 1





    When you modify an object - list, dictionary, ndarray, and append it to a list, you need to append a copy, not the object that you keep modifying. Otherwise, all elements of the list will end up looking the same - because they are the same object. List append does not automatically save a copy; you have to do that yourself.

    – hpaulj
    Mar 25 at 6:59













3












3








3


1






I'm having problem with this code. I think I'm doing something wrong.



 import numpy as np

array = np.zeros(10)

arrays = []

for i in range(len(array)):
array[i] = 1
arrays.append(array)

print(arrays[0])


I was expecting to get:[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
But I'm getting:[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]



That is the last array I appended to arrays, and not the first one. Why is that happening and more important what can I do to get the desired output?










share|improve this question
















I'm having problem with this code. I think I'm doing something wrong.



 import numpy as np

array = np.zeros(10)

arrays = []

for i in range(len(array)):
array[i] = 1
arrays.append(array)

print(arrays[0])


I was expecting to get:[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
But I'm getting:[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]



That is the last array I appended to arrays, and not the first one. Why is that happening and more important what can I do to get the desired output?







python numpy






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Mar 25 at 7:13









hpaulj

122k792167




122k792167










asked Mar 25 at 4:54









Alejandro RuizAlejandro Ruiz

297




297







  • 3





    Hint: how many different array are there in your code? How many arrays do you create?

    – Jörg W Mittag
    Mar 25 at 4:56






  • 1





    Also why not using np.eye?

    – Julien
    Mar 25 at 4:58











  • convert array to nympy array

    – prashant rana
    Mar 25 at 4:58











  • I used a simple example to show what's happening, in reality I'm using array of matrices with values I get from data, but the problem is the same.

    – Alejandro Ruiz
    Mar 25 at 5:02






  • 1





    When you modify an object - list, dictionary, ndarray, and append it to a list, you need to append a copy, not the object that you keep modifying. Otherwise, all elements of the list will end up looking the same - because they are the same object. List append does not automatically save a copy; you have to do that yourself.

    – hpaulj
    Mar 25 at 6:59












  • 3





    Hint: how many different array are there in your code? How many arrays do you create?

    – Jörg W Mittag
    Mar 25 at 4:56






  • 1





    Also why not using np.eye?

    – Julien
    Mar 25 at 4:58











  • convert array to nympy array

    – prashant rana
    Mar 25 at 4:58











  • I used a simple example to show what's happening, in reality I'm using array of matrices with values I get from data, but the problem is the same.

    – Alejandro Ruiz
    Mar 25 at 5:02






  • 1





    When you modify an object - list, dictionary, ndarray, and append it to a list, you need to append a copy, not the object that you keep modifying. Otherwise, all elements of the list will end up looking the same - because they are the same object. List append does not automatically save a copy; you have to do that yourself.

    – hpaulj
    Mar 25 at 6:59







3




3





Hint: how many different array are there in your code? How many arrays do you create?

– Jörg W Mittag
Mar 25 at 4:56





Hint: how many different array are there in your code? How many arrays do you create?

– Jörg W Mittag
Mar 25 at 4:56




1




1





Also why not using np.eye?

– Julien
Mar 25 at 4:58





Also why not using np.eye?

– Julien
Mar 25 at 4:58













convert array to nympy array

– prashant rana
Mar 25 at 4:58





convert array to nympy array

– prashant rana
Mar 25 at 4:58













I used a simple example to show what's happening, in reality I'm using array of matrices with values I get from data, but the problem is the same.

– Alejandro Ruiz
Mar 25 at 5:02





I used a simple example to show what's happening, in reality I'm using array of matrices with values I get from data, but the problem is the same.

– Alejandro Ruiz
Mar 25 at 5:02




1




1





When you modify an object - list, dictionary, ndarray, and append it to a list, you need to append a copy, not the object that you keep modifying. Otherwise, all elements of the list will end up looking the same - because they are the same object. List append does not automatically save a copy; you have to do that yourself.

– hpaulj
Mar 25 at 6:59





When you modify an object - list, dictionary, ndarray, and append it to a list, you need to append a copy, not the object that you keep modifying. Otherwise, all elements of the list will end up looking the same - because they are the same object. List append does not automatically save a copy; you have to do that yourself.

– hpaulj
Mar 25 at 6:59












5 Answers
5






active

oldest

votes


















4














I think you are expecting:



arrays.append(array)


to add a COPY of your main array to the arrays list. But that's not what you're doing. You're pushing another reference to the same array each time you do:



arrays.append(array)


so at the end of your loop, you have the list arrays with 10 references to the same original array you created. By then, you've set every value of that ONE ARRAY to 1. So you get that the first value in arrays contains an array with every value set to 1 because every array in arrays is that same array.



If you actually copy a new array each time into arrays, I bet you'll get what you expected. To do that, change that line to:



arrays.append(array.copy())


Here's a complete version of your program with this fix. I changed it also to print all 10 of the arrays in arrays:



def main():
import numpy as np

array = np.zeros(10)

arrays = []

for i in range(len(array)):
array[i] = 1
arrays.append(array.copy())

for array in arrays:
print(array)


Result:



[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[1. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
[1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]
[1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
[1. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
[1. 1. 1. 1. 1. 1. 1. 0. 0. 0.]
[1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]





share|improve this answer

























  • array is not a list; it's a NumPy array. They look similar, but the types are very different and should not be mixed up.

    – user2357112
    Mar 25 at 5:33











  • Doh! Thanks for pointing that out. Then I showed how to fix it incorrectly, lol! I don't know numpy. I showed the problem, and the idea of how to fix it. If the guy really needs a numpy array, I hope he'd be able to fix this himself. Still...you're right...I'll amend my answer. - guess my output looking different when printed should have tipped me off. I don't act all that bright sometimes. Thanks again!

    – Steve
    Mar 25 at 5:44












  • @AlejandroRuiz If this solved your problem, please consider accepting it. See also help

    – tripleee
    Mar 25 at 11:14


















0














just add this change:



arrays.append(np.array(array))





share|improve this answer

























  • You made the same mistake I did originally in my answer. array isn't a list...it's a numpy array. So here, you're changing the type of the container, not just copying it. Like I said, I made the same mistake originally. It took @user2357112 to point out the error of my ways.

    – Steve
    Mar 25 at 5:58











  • you are right.I have change it to np.array().

    – Ali Hallaji
    Mar 25 at 6:09


















0














The actual way to do this in numpy is with np.tri():



np.tri(10)
Out[]:
array([[ 1., 0., 0., ..., 0., 0., 0.],
[ 1., 1., 0., ..., 0., 0., 0.],
[ 1., 1., 1., ..., 0., 0., 0.],
...,
[ 1., 1., 1., ..., 1., 0., 0.],
[ 1., 1., 1., ..., 1., 1., 0.],
[ 1., 1., 1., ..., 1., 1., 1.]])





share|improve this answer
































    -1














    Maybe you are looking for this , just added if condition in your code



    import numpy as np

    array = np.zeros(10)

    arrays = []

    for i in range(len(array)):
    if i==0:
    array[i] = 1
    arrays.append(array)

    print(arrays[0])
    out: [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]





    share|improve this answer






























      -1














      You can use array.copy() a method defined on numpy arrays as @Steve has suggested.




      As it has been already used in one of the answer (@Steve's answer) to this problem so I choose another approach i.e. deepcopy() functionto obtain the result.




      import numpy as np 
      from copy import deepcopy

      array = np.zeros(10)
      arrays = []

      for i in range(len(array)):
      array[i] = 1
      arrays.append(deepcopy(array))

      print(arrays)
      # [array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])]

      print(arrays[0])
      # [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

      print(arrays[-1])
      # [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]





      share|improve this answer

























      • Thank you for your comment @tripleee . I have updated my answer. I know array.copy() is good choice but it is already a part of 1 answer so used deepcopy() to get the result.

        – hygull
        Mar 25 at 11:05












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      5 Answers
      5






      active

      oldest

      votes








      5 Answers
      5






      active

      oldest

      votes









      active

      oldest

      votes






      active

      oldest

      votes









      4














      I think you are expecting:



      arrays.append(array)


      to add a COPY of your main array to the arrays list. But that's not what you're doing. You're pushing another reference to the same array each time you do:



      arrays.append(array)


      so at the end of your loop, you have the list arrays with 10 references to the same original array you created. By then, you've set every value of that ONE ARRAY to 1. So you get that the first value in arrays contains an array with every value set to 1 because every array in arrays is that same array.



      If you actually copy a new array each time into arrays, I bet you'll get what you expected. To do that, change that line to:



      arrays.append(array.copy())


      Here's a complete version of your program with this fix. I changed it also to print all 10 of the arrays in arrays:



      def main():
      import numpy as np

      array = np.zeros(10)

      arrays = []

      for i in range(len(array)):
      array[i] = 1
      arrays.append(array.copy())

      for array in arrays:
      print(array)


      Result:



      [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]





      share|improve this answer

























      • array is not a list; it's a NumPy array. They look similar, but the types are very different and should not be mixed up.

        – user2357112
        Mar 25 at 5:33











      • Doh! Thanks for pointing that out. Then I showed how to fix it incorrectly, lol! I don't know numpy. I showed the problem, and the idea of how to fix it. If the guy really needs a numpy array, I hope he'd be able to fix this himself. Still...you're right...I'll amend my answer. - guess my output looking different when printed should have tipped me off. I don't act all that bright sometimes. Thanks again!

        – Steve
        Mar 25 at 5:44












      • @AlejandroRuiz If this solved your problem, please consider accepting it. See also help

        – tripleee
        Mar 25 at 11:14















      4














      I think you are expecting:



      arrays.append(array)


      to add a COPY of your main array to the arrays list. But that's not what you're doing. You're pushing another reference to the same array each time you do:



      arrays.append(array)


      so at the end of your loop, you have the list arrays with 10 references to the same original array you created. By then, you've set every value of that ONE ARRAY to 1. So you get that the first value in arrays contains an array with every value set to 1 because every array in arrays is that same array.



      If you actually copy a new array each time into arrays, I bet you'll get what you expected. To do that, change that line to:



      arrays.append(array.copy())


      Here's a complete version of your program with this fix. I changed it also to print all 10 of the arrays in arrays:



      def main():
      import numpy as np

      array = np.zeros(10)

      arrays = []

      for i in range(len(array)):
      array[i] = 1
      arrays.append(array.copy())

      for array in arrays:
      print(array)


      Result:



      [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]





      share|improve this answer

























      • array is not a list; it's a NumPy array. They look similar, but the types are very different and should not be mixed up.

        – user2357112
        Mar 25 at 5:33











      • Doh! Thanks for pointing that out. Then I showed how to fix it incorrectly, lol! I don't know numpy. I showed the problem, and the idea of how to fix it. If the guy really needs a numpy array, I hope he'd be able to fix this himself. Still...you're right...I'll amend my answer. - guess my output looking different when printed should have tipped me off. I don't act all that bright sometimes. Thanks again!

        – Steve
        Mar 25 at 5:44












      • @AlejandroRuiz If this solved your problem, please consider accepting it. See also help

        – tripleee
        Mar 25 at 11:14













      4












      4








      4







      I think you are expecting:



      arrays.append(array)


      to add a COPY of your main array to the arrays list. But that's not what you're doing. You're pushing another reference to the same array each time you do:



      arrays.append(array)


      so at the end of your loop, you have the list arrays with 10 references to the same original array you created. By then, you've set every value of that ONE ARRAY to 1. So you get that the first value in arrays contains an array with every value set to 1 because every array in arrays is that same array.



      If you actually copy a new array each time into arrays, I bet you'll get what you expected. To do that, change that line to:



      arrays.append(array.copy())


      Here's a complete version of your program with this fix. I changed it also to print all 10 of the arrays in arrays:



      def main():
      import numpy as np

      array = np.zeros(10)

      arrays = []

      for i in range(len(array)):
      array[i] = 1
      arrays.append(array.copy())

      for array in arrays:
      print(array)


      Result:



      [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]





      share|improve this answer















      I think you are expecting:



      arrays.append(array)


      to add a COPY of your main array to the arrays list. But that's not what you're doing. You're pushing another reference to the same array each time you do:



      arrays.append(array)


      so at the end of your loop, you have the list arrays with 10 references to the same original array you created. By then, you've set every value of that ONE ARRAY to 1. So you get that the first value in arrays contains an array with every value set to 1 because every array in arrays is that same array.



      If you actually copy a new array each time into arrays, I bet you'll get what you expected. To do that, change that line to:



      arrays.append(array.copy())


      Here's a complete version of your program with this fix. I changed it also to print all 10 of the arrays in arrays:



      def main():
      import numpy as np

      array = np.zeros(10)

      arrays = []

      for i in range(len(array)):
      array[i] = 1
      arrays.append(array.copy())

      for array in arrays:
      print(array)


      Result:



      [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 0. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 0. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 0. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]






      share|improve this answer














      share|improve this answer



      share|improve this answer








      edited Mar 25 at 5:51

























      answered Mar 25 at 5:10









      SteveSteve

      4,1801728




      4,1801728












      • array is not a list; it's a NumPy array. They look similar, but the types are very different and should not be mixed up.

        – user2357112
        Mar 25 at 5:33











      • Doh! Thanks for pointing that out. Then I showed how to fix it incorrectly, lol! I don't know numpy. I showed the problem, and the idea of how to fix it. If the guy really needs a numpy array, I hope he'd be able to fix this himself. Still...you're right...I'll amend my answer. - guess my output looking different when printed should have tipped me off. I don't act all that bright sometimes. Thanks again!

        – Steve
        Mar 25 at 5:44












      • @AlejandroRuiz If this solved your problem, please consider accepting it. See also help

        – tripleee
        Mar 25 at 11:14

















      • array is not a list; it's a NumPy array. They look similar, but the types are very different and should not be mixed up.

        – user2357112
        Mar 25 at 5:33











      • Doh! Thanks for pointing that out. Then I showed how to fix it incorrectly, lol! I don't know numpy. I showed the problem, and the idea of how to fix it. If the guy really needs a numpy array, I hope he'd be able to fix this himself. Still...you're right...I'll amend my answer. - guess my output looking different when printed should have tipped me off. I don't act all that bright sometimes. Thanks again!

        – Steve
        Mar 25 at 5:44












      • @AlejandroRuiz If this solved your problem, please consider accepting it. See also help

        – tripleee
        Mar 25 at 11:14
















      array is not a list; it's a NumPy array. They look similar, but the types are very different and should not be mixed up.

      – user2357112
      Mar 25 at 5:33





      array is not a list; it's a NumPy array. They look similar, but the types are very different and should not be mixed up.

      – user2357112
      Mar 25 at 5:33













      Doh! Thanks for pointing that out. Then I showed how to fix it incorrectly, lol! I don't know numpy. I showed the problem, and the idea of how to fix it. If the guy really needs a numpy array, I hope he'd be able to fix this himself. Still...you're right...I'll amend my answer. - guess my output looking different when printed should have tipped me off. I don't act all that bright sometimes. Thanks again!

      – Steve
      Mar 25 at 5:44






      Doh! Thanks for pointing that out. Then I showed how to fix it incorrectly, lol! I don't know numpy. I showed the problem, and the idea of how to fix it. If the guy really needs a numpy array, I hope he'd be able to fix this himself. Still...you're right...I'll amend my answer. - guess my output looking different when printed should have tipped me off. I don't act all that bright sometimes. Thanks again!

      – Steve
      Mar 25 at 5:44














      @AlejandroRuiz If this solved your problem, please consider accepting it. See also help

      – tripleee
      Mar 25 at 11:14





      @AlejandroRuiz If this solved your problem, please consider accepting it. See also help

      – tripleee
      Mar 25 at 11:14













      0














      just add this change:



      arrays.append(np.array(array))





      share|improve this answer

























      • You made the same mistake I did originally in my answer. array isn't a list...it's a numpy array. So here, you're changing the type of the container, not just copying it. Like I said, I made the same mistake originally. It took @user2357112 to point out the error of my ways.

        – Steve
        Mar 25 at 5:58











      • you are right.I have change it to np.array().

        – Ali Hallaji
        Mar 25 at 6:09















      0














      just add this change:



      arrays.append(np.array(array))





      share|improve this answer

























      • You made the same mistake I did originally in my answer. array isn't a list...it's a numpy array. So here, you're changing the type of the container, not just copying it. Like I said, I made the same mistake originally. It took @user2357112 to point out the error of my ways.

        – Steve
        Mar 25 at 5:58











      • you are right.I have change it to np.array().

        – Ali Hallaji
        Mar 25 at 6:09













      0












      0








      0







      just add this change:



      arrays.append(np.array(array))





      share|improve this answer















      just add this change:



      arrays.append(np.array(array))






      share|improve this answer














      share|improve this answer



      share|improve this answer








      edited Mar 25 at 6:00

























      answered Mar 25 at 5:15









      Ali HallajiAli Hallaji

      9581119




      9581119












      • You made the same mistake I did originally in my answer. array isn't a list...it's a numpy array. So here, you're changing the type of the container, not just copying it. Like I said, I made the same mistake originally. It took @user2357112 to point out the error of my ways.

        – Steve
        Mar 25 at 5:58











      • you are right.I have change it to np.array().

        – Ali Hallaji
        Mar 25 at 6:09

















      • You made the same mistake I did originally in my answer. array isn't a list...it's a numpy array. So here, you're changing the type of the container, not just copying it. Like I said, I made the same mistake originally. It took @user2357112 to point out the error of my ways.

        – Steve
        Mar 25 at 5:58











      • you are right.I have change it to np.array().

        – Ali Hallaji
        Mar 25 at 6:09
















      You made the same mistake I did originally in my answer. array isn't a list...it's a numpy array. So here, you're changing the type of the container, not just copying it. Like I said, I made the same mistake originally. It took @user2357112 to point out the error of my ways.

      – Steve
      Mar 25 at 5:58





      You made the same mistake I did originally in my answer. array isn't a list...it's a numpy array. So here, you're changing the type of the container, not just copying it. Like I said, I made the same mistake originally. It took @user2357112 to point out the error of my ways.

      – Steve
      Mar 25 at 5:58













      you are right.I have change it to np.array().

      – Ali Hallaji
      Mar 25 at 6:09





      you are right.I have change it to np.array().

      – Ali Hallaji
      Mar 25 at 6:09











      0














      The actual way to do this in numpy is with np.tri():



      np.tri(10)
      Out[]:
      array([[ 1., 0., 0., ..., 0., 0., 0.],
      [ 1., 1., 0., ..., 0., 0., 0.],
      [ 1., 1., 1., ..., 0., 0., 0.],
      ...,
      [ 1., 1., 1., ..., 1., 0., 0.],
      [ 1., 1., 1., ..., 1., 1., 0.],
      [ 1., 1., 1., ..., 1., 1., 1.]])





      share|improve this answer





























        0














        The actual way to do this in numpy is with np.tri():



        np.tri(10)
        Out[]:
        array([[ 1., 0., 0., ..., 0., 0., 0.],
        [ 1., 1., 0., ..., 0., 0., 0.],
        [ 1., 1., 1., ..., 0., 0., 0.],
        ...,
        [ 1., 1., 1., ..., 1., 0., 0.],
        [ 1., 1., 1., ..., 1., 1., 0.],
        [ 1., 1., 1., ..., 1., 1., 1.]])





        share|improve this answer



























          0












          0








          0







          The actual way to do this in numpy is with np.tri():



          np.tri(10)
          Out[]:
          array([[ 1., 0., 0., ..., 0., 0., 0.],
          [ 1., 1., 0., ..., 0., 0., 0.],
          [ 1., 1., 1., ..., 0., 0., 0.],
          ...,
          [ 1., 1., 1., ..., 1., 0., 0.],
          [ 1., 1., 1., ..., 1., 1., 0.],
          [ 1., 1., 1., ..., 1., 1., 1.]])





          share|improve this answer















          The actual way to do this in numpy is with np.tri():



          np.tri(10)
          Out[]:
          array([[ 1., 0., 0., ..., 0., 0., 0.],
          [ 1., 1., 0., ..., 0., 0., 0.],
          [ 1., 1., 1., ..., 0., 0., 0.],
          ...,
          [ 1., 1., 1., ..., 1., 0., 0.],
          [ 1., 1., 1., ..., 1., 1., 0.],
          [ 1., 1., 1., ..., 1., 1., 1.]])






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Mar 25 at 10:51

























          answered Mar 25 at 10:46









          Daniel FDaniel F

          7,5621432




          7,5621432





















              -1














              Maybe you are looking for this , just added if condition in your code



              import numpy as np

              array = np.zeros(10)

              arrays = []

              for i in range(len(array)):
              if i==0:
              array[i] = 1
              arrays.append(array)

              print(arrays[0])
              out: [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]





              share|improve this answer



























                -1














                Maybe you are looking for this , just added if condition in your code



                import numpy as np

                array = np.zeros(10)

                arrays = []

                for i in range(len(array)):
                if i==0:
                array[i] = 1
                arrays.append(array)

                print(arrays[0])
                out: [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]





                share|improve this answer

























                  -1












                  -1








                  -1







                  Maybe you are looking for this , just added if condition in your code



                  import numpy as np

                  array = np.zeros(10)

                  arrays = []

                  for i in range(len(array)):
                  if i==0:
                  array[i] = 1
                  arrays.append(array)

                  print(arrays[0])
                  out: [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]





                  share|improve this answer













                  Maybe you are looking for this , just added if condition in your code



                  import numpy as np

                  array = np.zeros(10)

                  arrays = []

                  for i in range(len(array)):
                  if i==0:
                  array[i] = 1
                  arrays.append(array)

                  print(arrays[0])
                  out: [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]






                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Mar 25 at 5:12









                  NickelNickel

                  1278




                  1278





















                      -1














                      You can use array.copy() a method defined on numpy arrays as @Steve has suggested.




                      As it has been already used in one of the answer (@Steve's answer) to this problem so I choose another approach i.e. deepcopy() functionto obtain the result.




                      import numpy as np 
                      from copy import deepcopy

                      array = np.zeros(10)
                      arrays = []

                      for i in range(len(array)):
                      array[i] = 1
                      arrays.append(deepcopy(array))

                      print(arrays)
                      # [array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])]

                      print(arrays[0])
                      # [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

                      print(arrays[-1])
                      # [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]





                      share|improve this answer

























                      • Thank you for your comment @tripleee . I have updated my answer. I know array.copy() is good choice but it is already a part of 1 answer so used deepcopy() to get the result.

                        – hygull
                        Mar 25 at 11:05
















                      -1














                      You can use array.copy() a method defined on numpy arrays as @Steve has suggested.




                      As it has been already used in one of the answer (@Steve's answer) to this problem so I choose another approach i.e. deepcopy() functionto obtain the result.




                      import numpy as np 
                      from copy import deepcopy

                      array = np.zeros(10)
                      arrays = []

                      for i in range(len(array)):
                      array[i] = 1
                      arrays.append(deepcopy(array))

                      print(arrays)
                      # [array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])]

                      print(arrays[0])
                      # [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

                      print(arrays[-1])
                      # [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]





                      share|improve this answer

























                      • Thank you for your comment @tripleee . I have updated my answer. I know array.copy() is good choice but it is already a part of 1 answer so used deepcopy() to get the result.

                        – hygull
                        Mar 25 at 11:05














                      -1












                      -1








                      -1







                      You can use array.copy() a method defined on numpy arrays as @Steve has suggested.




                      As it has been already used in one of the answer (@Steve's answer) to this problem so I choose another approach i.e. deepcopy() functionto obtain the result.




                      import numpy as np 
                      from copy import deepcopy

                      array = np.zeros(10)
                      arrays = []

                      for i in range(len(array)):
                      array[i] = 1
                      arrays.append(deepcopy(array))

                      print(arrays)
                      # [array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])]

                      print(arrays[0])
                      # [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

                      print(arrays[-1])
                      # [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]





                      share|improve this answer















                      You can use array.copy() a method defined on numpy arrays as @Steve has suggested.




                      As it has been already used in one of the answer (@Steve's answer) to this problem so I choose another approach i.e. deepcopy() functionto obtain the result.




                      import numpy as np 
                      from copy import deepcopy

                      array = np.zeros(10)
                      arrays = []

                      for i in range(len(array)):
                      array[i] = 1
                      arrays.append(deepcopy(array))

                      print(arrays)
                      # [array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 0., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 0., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 0., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 0., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 0., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 0., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 0.]), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])]

                      print(arrays[0])
                      # [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

                      print(arrays[-1])
                      # [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]






                      share|improve this answer














                      share|improve this answer



                      share|improve this answer








                      edited Mar 25 at 11:11

























                      answered Mar 25 at 5:05









                      hygullhygull

                      4,57021632




                      4,57021632












                      • Thank you for your comment @tripleee . I have updated my answer. I know array.copy() is good choice but it is already a part of 1 answer so used deepcopy() to get the result.

                        – hygull
                        Mar 25 at 11:05


















                      • Thank you for your comment @tripleee . I have updated my answer. I know array.copy() is good choice but it is already a part of 1 answer so used deepcopy() to get the result.

                        – hygull
                        Mar 25 at 11:05

















                      Thank you for your comment @tripleee . I have updated my answer. I know array.copy() is good choice but it is already a part of 1 answer so used deepcopy() to get the result.

                      – hygull
                      Mar 25 at 11:05






                      Thank you for your comment @tripleee . I have updated my answer. I know array.copy() is good choice but it is already a part of 1 answer so used deepcopy() to get the result.

                      – hygull
                      Mar 25 at 11:05


















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