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.)
This question is related to
python
arrays
matrix
numpy
multidimensional-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))
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)
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)
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]
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.
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
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)
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.
Source: Stackoverflow.com