np.unravel_index() for multi-dim positions in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get the multi-dimensional index of the flat index 5 in a 3x3 array.

NumPy

import numpy as np
index = np.unravel_index([1], (3, 3))
print(index)

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A1

B5

C9

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the shape as a single number instead of a tuple.

Confusing flat index with multi-dimensional index.

The flat index 5 corresponds to the multi-dimensional index (1, 2) in a 3x3 array.

2fill in blank

medium

Complete the code to find the multi-dimensional index of flat index 14 in a 4x5 array.

NumPy

import numpy as np
pos = np.unravel_index([1], (4, 5))
print(pos)

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A14

B10

C20

D9

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong flat index number.

Mixing up rows and columns in the shape.

Flat index 14 corresponds to position (2, 4) in a 4x5 array.

3fill in blank

hard

Fix the error in the code to correctly get the multi-dimensional index of flat index 7 in a 2x4x3 array.

NumPy

import numpy as np
pos = np.unravel_index([1], (2, 4, 3))
print(pos)

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A7

B[7]

C(7,)

D'7'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the flat index as a list or string.

Using incorrect shape tuple format.

The flat index must be an integer, not a list, tuple, or string.

4fill in blank

hard

Fill both blanks to create a list of multi-dimensional indices for flat indices 0 to 5 in a 2x3 array.

NumPy

import numpy as np
indices = [np.unravel_index(i, [1]) for i in [2]]
print(indices)

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A(2, 3)

Brange(6)

Crange(5)

D[0, 1, 2, 3, 4, 5]

Attempts:

3 left

💡 Hint

Common Mistakes

Using range(5) which excludes the last index.

Using a list instead of range for iteration.

The shape is (2, 3) and flat indices go from 0 to 5, so range(6) is used.

5fill in blank

hard

Fill all three blanks to create a dictionary mapping flat indices to their multi-dimensional indices for a 3x2x2 array, for indices 0 to 11.

NumPy

import numpy as np
mapping = { [1]: [2] for i in [3] }
print(mapping)

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Ai

Bnp.unravel_index(i, (3, 2, 2))

Crange(12)

Di**2

Attempts:

3 left

💡 Hint

Common Mistakes

Using i**2 instead of unravel_index for values.

Using incorrect range length.

Swapping keys and values.

Each flat index i maps to its multi-dimensional index using np.unravel_index over range(12) for a 3x2x2 array.