Convert 1d array to 2d python

I want to convert a 1-dimensional array into a 2-dimensional array by specifying the number of columns in the 2D array. Something that would work like this:

> import numpy as np
> A = np.array([1,2,3,4,5,6])
> B = vec2matrix(A,ncol=2)
> B
array([[1, 2],
       [3, 4],
       [5, 6]])

Does numpy have a function that works like my made-up function "vec2matrix"? (I understand that you can index a 1D array like a 2D array, but that isn't an option in the code I have - I need to make this conversion.)

asked Sep 25, 2012 at 2:23

You want to reshape the array.

B = np.reshape(A, (-1, 2))

where -1 infers the size of the new dimension from the size of the input array.

nbro

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answered Sep 25, 2012 at 2:27

Matt BallMatt Ball

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0

You have two options:

• If you no longer want the original shape, the easiest is just to assign a new shape to the array

  a.shape = (a.size//ncols, ncols)
  

  You can switch the a.size//ncols by -1 to compute the proper shape automatically. Make sure that a.shape[0]*a.shape[1]=a.size, else you'll run into some problem.

• You can get a new array with the np.reshape function, that works mostly like the version presented above

  new = np.reshape(a, (-1, ncols))
  

  When it's possible, new will be just a view of the initial array a, meaning that the data are shared. In some cases, though, new array will be acopy instead. Note that np.reshape also accepts an optional keyword order that lets you switch from row-major C order to column-major Fortran order. np.reshape is the function version of the a.reshape method.

If you can't respect the requirement a.shape[0]*a.shape[1]=a.size, you're stuck with having to create a new array. You can use the np.resize function and mixing it with np.reshape, such as

>>> a =np.arange(9)
>>> np.resize(a, 10).reshape(5,2)

answered Sep 25, 2012 at 8:03

Pierre GMPierre GM

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Try something like:

B = np.reshape(A,(-1,ncols))

You'll need to make sure that you can divide the number of elements in your array by ncols though. You can also play with the order in which the numbers are pulled into B using the order keyword.

answered Sep 25, 2012 at 4:19

JoshAdelJoshAdel

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If your sole purpose is to convert a 1d array X to a 2d array just do:

X = np.reshape(X,(1, X.size))

answered Jan 14, 2020 at 18:09

ArunArun

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convert a 1-dimensional array into a 2-dimensional array by adding new axis.

a=np.array([10,20,30,40,50,60])

b=a[:,np.newaxis]--it will convert it to two dimension.

derloopkat

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answered Jan 10, 2021 at 4:38

There is a simple way as well, we can use the reshape function in a different way:

A_reshape = A.reshape(No_of_rows, No_of_columns)

Tamás Sengel

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answered Jan 7, 2021 at 3:07

1

You can useflatten() from the numpy package.

import numpy as np
a = np.array([[1, 2],
       [3, 4],
       [5, 6]])
a_flat = a.flatten()
print(f"original array: {a} \nflattened array = {a_flat}")

Output:

original array: [[1 2]
 [3 4]
 [5 6]] 
flattened array = [1 2 3 4 5 6]

answered Mar 20, 2019 at 16:19

RafiRafi

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some_array.shape = (1,)+some_array.shape

or get a new one

another_array = numpy.reshape(some_array, (1,)+some_array.shape)

This will make dimensions +1, equals to adding a bracket on the outermost

answered Apr 18, 2020 at 2:27

ZDL-soZDL-so

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import numpy as np
array = np.arange(8) 
print("Original array : \n", array)
array = np.arange(8).reshape(2, 4)
print("New array : \n", array)

Milo

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answered Nov 25, 2019 at 14:38

2

Change 1D array into 2D array without using Numpy.

l = [i for i in range(1,21)]
part = 3
new = []
start, end = 0, part


while end <= len(l):
    temp = []
    for i in range(start, end):
        temp.append(l[i])
    new.append(temp)
    start += part
    end += part
print("new values:  ", new)


# for uneven cases
temp = []
while start < len(l):
    temp.append(l[start])
    start += 1
    new.append(temp)
print("new values for uneven cases:   ", new)

answered Sep 6, 2019 at 10:37

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