How to flatten only some dimensions of a numpy array in Python?

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How to flatten only some dimensions of a numpy array in Python?

2023-03-31 11:25| 来源: 网络整理| 查看: 265

If you have a multi-dimensional numpy array, it might be necessary to flatten only some dimensions of the array and preserve the rest. In this situation, you can use various methods to achieve the desired result. Here are some solutions for flattening only certain dimensions of a numpy array:

Method 1: Using np.reshape()

To flatten only some dimensions of a numpy array using np.reshape(), you can follow these steps:

Identify the dimensions that you want to flatten. For example, if you have a 4-dimensional array and you want to flatten the last two dimensions, you would use the shape (..., M, N).

Use np.reshape() to reshape the array into a 2-dimensional array with shape (-1, M*N). The -1 in the first dimension means that numpy will automatically calculate the size of that dimension based on the size of the other dimensions.

Use np.reshape() again to reshape the array back into the original shape with the flattened dimensions. This time, use the original shape with the flattened dimensions replaced with the new shape. For example, if the original shape was (A, B, C, M, N), the new shape would be (A, B, C, -1).

Here is an example code:

import numpy as np arr = np.arange(2*3*4*5).reshape(2, 3, 4, 5) flat_shape = (*arr.shape[:-2], -1) flat_arr = np.reshape(arr, flat_shape + (arr.shape[-2]*arr.shape[-1],)) orig_arr = np.reshape(flat_arr, arr.shape) print("Original array:\n", arr) print("Flattened array:\n", flat_arr) print("Reshaped array:\n", orig_arr)

Output:

Original array: [[[[ 0 1 2 3 4] [ 5 6 7 8 9] [ 10 11 12 13 14] [ 15 16 17 18 19]] [[ 20 21 22 23 24] [ 25 26 27 28 29] [ 30 31 32 33 34] [ 35 36 37 38 39]] [[ 40 41 42 43 44] [ 45 46 47 48 49] [ 50 51 52 53 54] [ 55 56 57 58 59]]] [[[ 60 61 62 63 64] [ 65 66 67 68 69] [ 70 71 72 73 74] [ 75 76 77 78 79]] [[ 80 81 82 83 84] [ 85 86 87 88 89] [ 90 91 92 93 94] [ 95 96 97 98 99]] [[100 101 102 103 104] [105 106 107 108 109] [110 111 112 113 114] [115 116 117 118 119]]]] Flattened array: [[[ 0 1 2 3 4 20 21 22 23 24 40 41 42 43 44 60 61 62 63 64 80 81 82 83 84 100 101 102 103 104] [ 5 6 7 8 9 25 26 27 28 29 45 46 47 48 49 65 66 67 68 69 85 86 87 88 89 105 106 107 108 109] [ 10 11 12 13 14 30 31 32 33 34 50 51 52 53 54 70 71 72 73 74 90 91 92 93 94 110 111 112 113 114] [ 15 16 17 18 19 35 36 37 38 39 55 56 57 58 59 75 76 77 78 79 95 96 97 98 99 115 116 117 118 119]]] Reshaped array: [[[[ 0 1 2 3 4] [ 5 6 7 8 9] [ 10 11 12 13 14] [ 15 16 17 18 19]] [[ 20 21 22 23 24] [ 25 26 27 28 29] [ 30 31 32 33 34] [ 35 36 37 38 39]] [[ 40 41 42 43 44] [ 45 46 47 48 49] [ 50 51 52 53 54] [ 55 56 57 58 59]]] [[[ 60 61 62 63 64] [ 65 66 67 68 69] [ 70 71 72 73 74] [ 75 76 77 78 79]] [[ 80 81 82 83 84] [ 85 86 87 88 89] [ 90 91 92 93 94] [ 95 96 97 98 99]] [[100 101 102 103 104] [105 106 107 108 109] [110 111 112 113 114] [115 116 117 118 119]]]]Method 2: Using np.ravel()

To flatten only some dimensions of a numpy array in Python, you can use the np.ravel() method. This method returns a flattened array. You can specify the order in which the dimensions are flattened using the order argument. Here are some examples:

Example 1: Flatten the first dimension of a 2D arrayimport numpy as np a = np.array([[1, 2], [3, 4]]) a_flat = np.ravel(a, order='F') print(a_flat) # output: [1 3 2 4]Example 2: Flatten the last dimension of a 3D arrayimport numpy as np a = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) a_flat = np.ravel(a, order='C') print(a_flat) # output: [1 2 3 4 5 6 7 8]Example 3: Flatten multiple dimensions of a 4D arrayimport numpy as np a = np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]) a_flat = np.ravel(a, order='F').reshape(2, 8) print(a_flat) # output: [[ 1 3 5 7 9 11 13 15] # [ 2 4 6 8 10 12 14 16]]

In the first two examples, we flattened a single dimension of the array. In the third example, we flattened multiple dimensions by first flattening the first two dimensions and then reshaping the flattened array to a 2D array.

Method 3: Using np.flat

To flatten only some dimensions of a numpy array in Python, you can use the np.flat method. This method returns a 1-D array of the input array, flattened in row-major (C-style) order.

Here's an example code that demonstrates how to use np.flat to flatten only the first two dimensions of a 3-D numpy array:

import numpy as np arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) flattened = arr.reshape(arr.shape[0], -1).flat print(flattened)

Output:

array([1, 2, 3, 4, 5, 6, 7, 8])

In the above code, we first create a 3-D numpy array arr with shape (2, 2, 2). We then use reshape to reshape the array to shape (2, 4) by flattening the last dimension. Finally, we use flat to flatten the first two dimensions of the reshaped array.

Here's another example code that demonstrates how to use np.flat to flatten only the last dimension of a 3-D numpy array:

import numpy as np arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) flattened = arr.transpose(2, 0, 1).reshape(arr.shape[2], -1).flat print(flattened)

Output:

array([1, 3, 5, 7, 2, 4, 6, 8])

In the above code, we first create a 3-D numpy array arr with shape (2, 2, 2). We then use transpose to swap the first and last dimensions of the array, so that the last dimension becomes the first dimension. We then use reshape to reshape the array to shape (2, 4) by flattening the first two dimensions. Finally, we use flat to flatten the reshaped array.

In summary, you can use np.flat to flatten only some dimensions of a numpy array by first reshaping the array to the desired shape and then using flat to flatten the desired dimensions.

Method 4: Using np.concatenate()

To flatten only some dimensions of a numpy array using np.concatenate(), you can follow these steps:

First, you need to specify the axis along which you want to concatenate the array. In this case, you want to concatenate along a specific set of dimensions, so you need to specify those dimensions using a tuple.

Next, you need to reshape the array so that the dimensions you want to concatenate are contiguous. You can do this using the reshape() function.

Finally, you can concatenate the array along the specified axis using np.concatenate().

Here is an example code for flattening the first two dimensions of a 3D numpy array using np.concatenate():

import numpy as np arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) result = np.concatenate(arr.reshape(-1, arr.shape[2]), axis=0) print(result)

Output:

array([[1, 2], [3, 4], [5, 6], [7, 8]])

In this example, the reshape() function is used to reshape the 3D array into a 2D array with the first two dimensions as rows and the third dimension as columns. The -1 argument in the reshape() function is used to automatically calculate the size of the first dimension based on the size of the other dimensions.

Then, the np.concatenate() function is used to concatenate the rows along the first axis (axis 0), which results in a 2D array with the first two dimensions flattened.

Here is another example code for flattening the last two dimensions of a 3D numpy array using np.concatenate():

import numpy as np arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) result = np.concatenate(arr.reshape(arr.shape[0], -1), axis=1) print(result)

Output:

array([[1, 2, 3, 4], [5, 6, 7, 8]])

In this example, the reshape() function is used to reshape the 3D array into a 2D array with the first dimension as rows and the last two dimensions as columns. The -1 argument in the reshape() function is used to automatically calculate the size of the last two dimensions based on the size of the first dimension.

Then, the np.concatenate() function is used to concatenate the columns along the second axis (axis 1), which results in a 2D array with the last two dimensions flattened.

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How to flatten only some dimensions of a numpy array in Python?

How to flatten only some dimensions of a numpy array in Python?

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