Convert a 1D array to a 2D array in numpy

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.)

Answers:

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Method 1

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.

Method 2

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)

Method 3

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.

Method 4

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

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

Method 5

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.

Method 6

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)

Method 7

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]

Method 8

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

Method 9

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)

Method 10

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)

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0