Array Manipulation Routines¶
Array manipulation is the art of reshaping, rearranging, combining, and splitting arrays without changing their underlying data. These operations are essential in data science pipelines where data arrives in one shape but needs to be in another for processing. Functions like reshape(), transpose(), concatenate(), split(), tile(), repeat(), and np.unique() let you restructure data efficiently. In deep learning, reshaping tensors and permuting dimensions is a daily task when moving data between layers that expect different input formats.
import numpy as np
np.__version__
Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. Reshape x so that the size of the second dimension equals 150.
Q2. Let x be array [[1, 2, 3], [4, 5, 6]]. Convert it to [1 4 2 5 3 6].
Q3. Let x be array [[1, 2, 3], [4, 5, 6]]. Get the 5th element.
Q4. Let x be an arbitrary 3-D array of shape (3, 4, 5). Permute the dimensions of x such that the new shape will be (4,3,5).
Q5. Let x be an arbitrary 2-D array of shape (3, 4). Permute the dimensions of x such that the new shape will be (4,3).
Q5. Let x be an arbitrary 2-D array of shape (3, 4). Insert a nex axis such that the new shape will be (3, 1, 4).
Q6. Let x be an arbitrary 3-D array of shape (3, 4, 1). Remove a single-dimensional entries such that the new shape will be (3, 4).
Q7. Lex x be an array
[[ 1 2 3]
[ 4 5 6].
and y be an array
[[ 7 8 9]
[10 11 12]].
Concatenate x and y so that a new array looks like
[[1, 2, 3, 7, 8, 9],
[4, 5, 6, 10, 11, 12]].
Q8. Lex x be an array
[[ 1 2 3]
[ 4 5 6].
and y be an array
[[ 7 8 9]
[10 11 12]].
Concatenate x and y so that a new array looks like
[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]
Q8. Let x be an array [1 2 3] and y be [4 5 6]. Convert it to [[1, 4], [2, 5], [3, 6]].
Q9. Let x be an array [[1],[2],[3]] and y be [[4], [5], [6]]. Convert x to [[[1, 4]], [[2, 5]], [[3, 6]]].
Q10. Let x be an array [1, 2, 3, …, 9]. Split x into 3 arrays, each of which has 4, 2, and 3 elements in the original order.
Q11. Let x be an array
[[[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.]],
[[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]]].
Split it into two such that the first array looks like
[[[ 0., 1., 2.],
[ 4., 5., 6.]],
[[ 8., 9., 10.],
[ 12., 13., 14.]]].
and the second one look like:
[[[ 3.],
[ 7.]],
[[ 11.],
[ 15.]]].
Q12. Let x be an array
[[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]].
Split it into two arrays along the second axis.
Q13. Let x be an array
[[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]].
Split it into two arrays along the first axis.
Q14. Let x be an array [0, 1, 2]. Convert it to
[[0, 1, 2, 0, 1, 2],
[0, 1, 2, 0, 1, 2]].
Q15. Let x be an array [0, 1, 2]. Convert it to
[0, 0, 1, 1, 2, 2].
Q16. Let x be an array [0, 0, 0, 1, 2, 3, 0, 2, 1, 0].
remove the leading the trailing zeros.
Q17. Let x be an array [2, 2, 1, 5, 4, 5, 1, 2, 3]. Get two arrays of unique elements and their counts.
Q18. Lex x be an array
[[ 1 2]
[ 3 4].
Flip x along the second axis.
Q19. Lex x be an array
[[ 1 2]
[ 3 4].
Flip x along the first axis.
Q20. Lex x be an array
[[ 1 2]
[ 3 4].
Rotate x 90 degrees counter-clockwise.
Q21 Lex x be an array
[[ 1 2 3 4]
[ 5 6 7 8].
Shift elements one step to right along the second axis.