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Reshaping
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Sometimes situations arise when we need to somehow change our array, for example, change the size of the array or go from an array of one dimension to an array of another dimension, but with the same data that was originally used. But it is not always convenient to recreate the array from scratch, so some functions modify the array as we need it.
Let's have a look at some of them:
np.reshape()- this function changes the shape of an N-dimensional array while maintaining the same total number of elements;np.transpose()- this function transposes the array, essentially swapping its axes;np.concatenate()- this function creates a new array by appending arrays one after another along the specified axis;np.resize()- this function is used to resize an array, creating a copy of the original array with the specified size.
Reshape one-dimensional array into a two-dimensional array:
123456import numpy as np array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) new_array = array.reshape(4, 3) print(new_array)
Reshape one-dimensional into a three-dimensional array:
123456import numpy as np array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) new_array = array.reshape(2, 3, 2) print(new_array)
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Consider the following array: [11, 56, 78, 45, 1, 5].
You should obtain the following array:
[[11, 56], [78, 45], [1, 5]].
Please use the .reshape() method.
解答
実践的な練習のためにデスクトップに切り替える下記のオプションのいずれかを利用して、現在の場所から続行するフィードバックありがとうございます！
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