Convert a 1D array to a 2D array in numpy
Question:
I want to convert a 1dimensional array into a 2dimensional 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 madeup 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:
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.
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.
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
by1
to compute the proper shape automatically. Make sure thata.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 abovenew = np.reshape(a, (1, ncols))
When it’s possible,
new
will be just a view of the initial arraya
, meaning that the data are shared. In some cases, though,new
array will be acopy instead. Note thatnp.reshape
also accepts an optional keywordorder
that lets you switch from rowmajor C order to columnmajor Fortran order.np.reshape
is the function version of thea.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)
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]
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)
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)
If your sole purpose is to convert a 1d array X to a 2d array just do:
X = np.reshape(X,(1, X.size))
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
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)
convert a 1dimensional array into a 2dimensional 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.