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)

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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.

Practice

1. What does np.unravel_index() do in NumPy?

easy

A. It returns the total number of elements in an array.

B. It converts a flat index into a tuple of coordinate indices for a given shape.

C. It reshapes an array into a new shape without changing data.

D. It flattens a multi-dimensional array into a one-dimensional array.

Solution

1. Step 1: Understand the function purpose

   np.unravel_index() takes a flat index and a shape to find the multi-dimensional coordinates.

2. Step 2: Compare options with function behavior

   Only It converts a flat index into a tuple of coordinate indices for a given shape. describes converting a flat index to multi-dimensional indices, which matches the function's purpose.

3. Final Answer:

   It converts a flat index into a tuple of coordinate indices for a given shape. -> Option B

4. Quick Check:

   Function purpose = convert flat index to coordinates [OK]

Hint: Remember: unravel means 'find coordinates' from flat index [OK]

Common Mistakes:

• Confusing unravel_index with flatten or reshape functions
• Thinking it returns the total number of elements
• Assuming it changes the array data

2. Which of the following is the correct syntax to use np.unravel_index() for a flat index 5 in a shape (3, 3)?

easy

A. np.unravel_index((3, 3), 5)

B. np.unravel_index(5, 3, 3)

C. np.unravel_index(5, (3, 3))

D. np.unravel_index([5], (3, 3))

Solution

1. Step 1: Recall the function signature

   The correct syntax is np.unravel_index(indices, shape), where indices is an integer or array and shape is a tuple.

2. Step 2: Match options to syntax

   np.unravel_index(5, (3, 3)) matches the correct syntax with index 5 and shape (3, 3). np.unravel_index([5], (3, 3)) uses a list for index which is allowed but less common for single index.

3. Final Answer:

   np.unravel_index(5, (3, 3)) -> Option C

4. Quick Check:

   Syntax = np.unravel_index(index, shape) [OK]

Hint: Use np.unravel_index(index, shape) with shape as a tuple [OK]

Common Mistakes:

• Swapping the order of arguments
• Passing shape as separate numbers instead of a tuple
• Using list instead of integer for single index without need

3. What is the output of the following code?

import numpy as np
index = 7
shape = (3, 3)
result = np.unravel_index(index, shape)
print(result)

medium

A. (1, 1)

B. (1, 2)

C. (2, 0)

D. (2, 1)

Solution

1. Step 1: Understand the shape and flat index

   The shape is (3, 3), so the array has 3 rows and 3 columns. Flat indices go row-wise: 0 to 8.

2. Step 2: Calculate multi-dimensional indices for flat index 7

   Index 7 corresponds to row = 7 // 3 = 2, column = 7 % 3 = 1, so position is (2, 1).

3. Final Answer:

   (2, 1) -> Option D

4. Quick Check:

   Row = 7//3=2, Col = 7%3=1 [OK]

Hint: Divide index by shape dims to find row, mod for column [OK]

Common Mistakes:

• Mixing row and column order
• Using zero-based index incorrectly
• Confusing division and modulo operations

4. The code below raises an error. What is the cause?

import numpy as np
index = 10
shape = (3, 3)
result = np.unravel_index(index, shape)
print(result)

medium

A. Index 10 is out of bounds for shape (3, 3).

B. Shape must be a list, not a tuple.

C. np.unravel_index requires a 1D array, not a scalar index.

D. Missing import statement for numpy.

Solution

1. Step 1: Check the shape size

   Shape (3, 3) means total elements = 3*3 = 9, so valid flat indices are 0 to 8.

2. Step 2: Analyze the index value

   Index 10 is greater than 8, so it is out of bounds and causes an error.

3. Final Answer:

   Index 10 is out of bounds for shape (3, 3). -> Option A

4. Quick Check:

   Max index = 8, given index = 10 [OK]

Hint: Check if index < product of shape dims to avoid errors [OK]

Common Mistakes:

• Assuming tuple shape is invalid
• Thinking np.unravel_index needs array input only
• Ignoring index bounds for given shape

5. You have a 3D array with shape (4, 3, 2). You want to find the multi-dimensional indices for flat indices [5, 10, 15]. Which code correctly returns these positions?

hard

A. np.unravel_index([5, 10, 15], (4, 3, 2))

B. np.unravel_index(5, 10, 15, (4, 3, 2))

C. np.unravel_index((5, 10, 15), (4, 3, 2))

D. np.unravel_index([5, 10, 15], 4, 3, 2)

Solution

1. Step 1: Understand input for multiple indices

   To convert multiple flat indices, pass a list or array of indices as first argument and shape tuple as second.

2. Step 2: Match options to correct syntax

   np.unravel_index([5, 10, 15], (4, 3, 2)) correctly passes a list of indices and the shape tuple. np.unravel_index((5, 10, 15), (4, 3, 2)) passes a tuple instead of list, which also works but less common. np.unravel_index(5, 10, 15, (4, 3, 2)) and D have wrong argument formats.

3. Final Answer:

   np.unravel_index([5, 10, 15], (4, 3, 2)) -> Option A

4. Quick Check:

   Multiple indices = list + shape tuple [OK]

Hint: Use list of indices and shape tuple for multiple positions [OK]

Common Mistakes:

• Passing multiple indices as separate arguments
• Using tuple instead of list for indices (less common but works)
• Passing shape as separate numbers instead of tuple