How to duplicate values inside a numpy array?

Question

i am looking for a way to duplicate value inside a numpy array in python according how many element it has inside (Len(-)). It looks like this:

a = [ [1]
      [2]
      [3]
    ]

after duplicating the values inside:

a = [ [1]
      [2]
      [3]
      [1]
      [2]
      [3]
      [1]
      [2]
      [3]
    ]

it is only an simple example. I need this to be done on a big data set.

Despite accepting an answer it appears that your question is incomplete. — hpaulj
– hpaulj, Commented Sep 13, 2020 at 20:27
If you choose to use np.tile, pay attention to the number of dimensions of your array, and the functions docs. — hpaulj
– hpaulj, Commented Sep 14, 2020 at 5:41

ashraful16 · Accepted Answer · 2020-09-13 12:47:47Z

2

You can try with list comprehension. Like this,

import numpy as np

a =np.array( [ [1],  [2], [3],])

arr = [[list(a[i]) for i in range(len(a))] for i in range(len(a))]
print(arr)

If you want to in 2-d array you can also try in this way,

a =np.array( [ [1],[2], [3],])

arr = [[list(a[i]) for i in range(len(a))] for i in range(len(a))]
arr = sum(arr, [])
print(arr)       # [[1], [2], [3], [1], [2], [3], [1], [2], [3]]

edited Sep 13, 2020 at 12:47

answered Sep 13, 2020 at 12:30

ashraful16

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3 Comments

Milad Over a year ago

thanks for the answer. I did use your code and then convert the list to an numpy array as follow: new_testing_images = np.asarray(tmp_testing_images, dtype=np.float32) now I expected to have a shape of (9, 28,28,1) but I got (3,3,28,28,1) why is that?

hpaulj Over a year ago

If you got a 5d array like that, then you clearly didn't start with a (n,1) array (as per your example). It's hard to guess what your really started with, or what kind of expansion you required. If this really is a numpy array, why did you accept a list comprehension solution?

Milad Over a year ago

second solution works fine actually. At the end I should convert the list to a numpy array.

hpaulj · Accepted Answer · 2020-09-14 05:40:09Z

0

In [1]: a = np.arange(1,4).reshape(3,1)
In [2]: a
Out[2]: 
array([[1],
       [2],
       [3]])
In [3]: np.tile(a,(3,1))
Out[3]: 
array([[1],
       [2],
       [3],
       [1],
       [2],
       [3],
       [1],
       [2],
       [3]])

The tile arguments will need to be adjusted if your array has more than 2 dimensions. Read its docs.

edited Sep 14, 2020 at 5:40

answered Sep 13, 2020 at 16:05

hpaulj

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