How to efficiently segment lists into fixed number of sublists in Python?

Efficiently segment Python lists with NumPy

In Python programming, it is often necessary to split a long list into multiple sublists of the same size. This article introduces two efficient ways to achieve this goal using the NumPy library and answers the question "How to split a list into a fixed number of sublists with NumPy".

Suppose there is a list of 30 elements, and you need to split it into 3 sublists of size 10. NumPy's reshape function and array_split function can be easily implemented.

Method 1: Use the reshape function

reshape function can change the shape of the array as long as the total number of elements remains unchanged. The code is as follows:

 import numpy as np

data = list(range(30)) # Create a list containing 0-29 result = np.array(data).reshape((3, 10))
print(result)

This code first converts the list to a NumPy array, and then reshapes it into a 2D array of 3 rows and 10 columns using reshape((3, 10)) , resulting in 3 sublists of size 10.

Method 2: Use array_split function

The array_split function can split an array into multiple subarrays. If the length of the array cannot be divisible by the number of splits, the length of the last subarrays may be different from that of the other subarrays. The code is as follows:

 import numpy as np

data = list(range(30))
result = np.array_split(data, 3)
print(result)

This code divides the list into 3 sublists. Since the list length (30) can be divisible by the number of segments (3), each sublist length is 10.

Both methods can effectively split the list, and which method to choose depends on the specific situation. reshape method is suitable for situations where the list length can be divisible by the number of segments, while the array_split method is more general and can handle situations where the length cannot be divisible.

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