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stackoverflow.com › questions › 16856788 › slice-2d-array-into-smaller-2d-arrays

There was another question a couple of months ago which clued me in to the idea of using reshape and swapaxes. The h//nrows makes sense since this keeps the first block's rows together. It also makes sense that you'll need nrows and ncols to be part of the shape. -1 tells reshape to fill in whatever number is necessary to make the reshape valid. Armed with the form of the solution, I just tried things until I found the formula that works.

You should be able to break your array into "blocks" using some combination of reshape and swapaxes:

def blockshaped(arr, nrows, ncols):
    """
    Return an array of shape (n, nrows, ncols) where
    n * nrows * ncols = arr.size

    If arr is a 2D array, the returned array should look like n subblocks with
    each subblock preserving the "physical" layout of arr.
    """
    h, w = arr.shape
    assert h % nrows == 0, f"{h} rows is not evenly divisible by {nrows}"
    assert w % ncols == 0, f"{w} cols is not evenly divisible by {ncols}"
    return (arr.reshape(h//nrows, nrows, -1, ncols)
               .swapaxes(1,2)
               .reshape(-1, nrows, ncols))

turns c

np.random.seed(365)
c = np.arange(24).reshape((4, 6))
print(c)

[out]:
[[ 0  1  2  3  4  5]
 [ 6  7  8  9 10 11]
 [12 13 14 15 16 17]
 [18 19 20 21 22 23]]

into

print(blockshaped(c, 2, 3))

[out]:
[[[ 0  1  2]
  [ 6  7  8]]

 [[ 3  4  5]
  [ 9 10 11]]

 [[12 13 14]
  [18 19 20]]

 [[15 16 17]
  [21 22 23]]]

I've posted an inverse function, unblockshaped, here, and an N-dimensional generalization here. The generalization gives a little more insight into the reasoning behind this algorithm.

Note that there is also superbatfish's blockwise_view. It arranges the blocks in a different format (using more axes) but it has the advantage of (1) always returning a view and (2) being capable of handling arrays of any dimension.

Answer from unutbu on Stack Overflow

NumPy

numpy.org › doc › stable › reference › generated › numpy.split.html

numpy.split — NumPy v2.5 Manual

Split an array into multiple sub-arrays as views into ary.

Stack Overflow

stackoverflow.com › questions › 16856788 › slice-2d-array-into-smaller-2d-arrays

python - Slice 2d array into smaller 2d arrays - Stack Overflow

Top answer

1 of 12

110

There was another question a couple of months ago which clued me in to the idea of using reshape and swapaxes. The h//nrows makes sense since this keeps the first block's rows together. It also makes sense that you'll need nrows and ncols to be part of the shape. -1 tells reshape to fill in whatever number is necessary to make the reshape valid. Armed with the form of the solution, I just tried things until I found the formula that works.

You should be able to break your array into "blocks" using some combination of reshape and swapaxes:

def blockshaped(arr, nrows, ncols):
    """
    Return an array of shape (n, nrows, ncols) where
    n * nrows * ncols = arr.size

    If arr is a 2D array, the returned array should look like n subblocks with
    each subblock preserving the "physical" layout of arr.
    """
    h, w = arr.shape
    assert h % nrows == 0, f"{h} rows is not evenly divisible by {nrows}"
    assert w % ncols == 0, f"{w} cols is not evenly divisible by {ncols}"
    return (arr.reshape(h//nrows, nrows, -1, ncols)
               .swapaxes(1,2)
               .reshape(-1, nrows, ncols))

turns c

np.random.seed(365)
c = np.arange(24).reshape((4, 6))
print(c)

[out]:
[[ 0  1  2  3  4  5]
 [ 6  7  8  9 10 11]
 [12 13 14 15 16 17]
 [18 19 20 21 22 23]]

into

print(blockshaped(c, 2, 3))

[out]:
[[[ 0  1  2]
  [ 6  7  8]]

 [[ 3  4  5]
  [ 9 10 11]]

 [[12 13 14]
  [18 19 20]]

 [[15 16 17]
  [21 22 23]]]

I've posted an inverse function, unblockshaped, here, and an N-dimensional generalization here. The generalization gives a little more insight into the reasoning behind this algorithm.

Note that there is also superbatfish's blockwise_view. It arranges the blocks in a different format (using more axes) but it has the advantage of (1) always returning a view and (2) being capable of handling arrays of any dimension.

2 of 12

8

It seems to me that this is a task for numpy.split or some variant.

e.g.

a = np.arange(30).reshape([5,6])  #a.shape = (5,6)
a1 = np.split(a,3,axis=1) 
#'a1' is a list of 3 arrays of shape (5,2)
a2 = np.split(a, [2,4])
#'a2' is a list of three arrays of shape (2,5), (2,5), (1,5)

If you have a NxN image you can create, e.g., a list of 2 NxN/2 subimages, and then divide them along the other axis.

numpy.hsplit and numpy.vsplit are also available.

python - Split a 2D numpy array where uneven splits are possible

python 3.x - Splitting a 2D numpy array into blocks based on row

python - Numpy slicing 2 columns from 2D array

python - Efficient way to slice a 2D array and skip indices; extract small segments repeatedly

python - Slicing of a NumPy 2d array, or how do I extract an mxm submatrix from an nxn array (n>m)?

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July 15, 2019 - Please let me know how to split a 2D numPy array with example code. Thanks

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reddit.com › r/askprogramming › split a 2d numpy array into 2 separate 2d arrays based on a column value

r/AskProgramming on Reddit: Split a 2d NumPy array into 2 separate 2d arrays based on a column value

October 19, 2020 -

So I have this 2d array that looks something like this

[[1,2,4],[1,4,4],[2,2,4],[2,5,9]]

The first column in this array can only ever be a 1 or a 2, I want to split this 2d array into 2 smaller 2d arrays, with all the arrays where the first column equals 1 are in and another where all the arrays whos column 1 has 2 in it. I am new to numpy and have seen some examples and documents online but i can't seem to get exactly what I want. Here is the current code I am trying to use

```

np.split(trainingData, np.where(trainingData[:, 0]== 1.)[0][1:])

```

I am basically trying to split the trainingData(which is my array) into 2 separate arrays, but when I run this I get more than 2 arrays. If someone could point me on the right path that would be great!

Thank you