np.unravel_index() for multi-dim positions in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.unravel_index with a single flat index

What is the output of the following code snippet?

NumPy

import numpy as np
index = 5
shape = (3, 4)
result = np.unravel_index(index, shape)
print(result)

A (1, 2)

B (0, 5)

C (2, 1)

D (1, 1)

💡 Hint

Remember that unravel_index converts a flat index to a tuple of coordinates in the given shape.

The flat index 5 corresponds to row 1, column 1 in a 3x4 array because 3 rows * 4 columns means indices go row-wise: 0 to 11. Index 5 is at position (1, 1).

❓ data_output

intermediate

Shape of output from np.unravel_index with multiple indices

Given multiple flat indices, what is the shape of the output from np.unravel_index?

NumPy

import numpy as np
indices = [3, 7, 11]
shape = (3, 4)
result = np.unravel_index(indices, shape)
print(result)

A A tuple of 2 arrays, each of shape (3,)

B A tuple of 3 arrays, each of shape (2,)

C A single array of shape (3, 2)

D A list of 3 tuples, each of length 2

💡 Hint

Check the documentation: unravel_index returns a tuple with one array per dimension.

For shape (3,4), unravel_index returns a tuple with 2 arrays (one per dimension). Each array has length equal to the number of indices (3 here).

🔧 Debug

advanced

Identify the error in using np.unravel_index with an invalid shape

What error does the following code raise?

NumPy

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

A ValueError: shape must be a tuple of positive integers

B IndexError: index 10 is out of bounds for array with size 9

C TypeError: index must be an integer or array of integers

D No error, output is (3, 1)

💡 Hint

Check if the flat index is valid for the given shape size.

The shape (3,3) means total size 9. Index 10 is out of bounds, so numpy raises IndexError.

🚀 Application

advanced

Using np.unravel_index to find coordinates of max value in a 3D array

You have a 3D numpy array. How can you find the coordinates of the maximum value using np.unravel_index?

NumPy

import numpy as np
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
max_index = arr.argmax()
coords = np.unravel_index(max_index, arr.shape)
print(coords)

A (1, 1, 1)

B (0, 1, 1)

C (1, 0, 1)

D (0, 0, 1)

💡 Hint

The maximum value is 8. Find its position in the 3D array.

The max value 8 is at position (1, 1, 1) in the array. argmax returns flat index 7, unravel_index converts it to (1,1,1).

🧠 Conceptual

expert

Understanding np.unravel_index output for high-dimensional arrays

For a 4D array with shape (2, 3, 4, 5), what is the length of the tuple returned by np.unravel_index for a single flat index?

A 5

B 1

C 4

D 20

💡 Hint

The tuple length equals the number of dimensions in the shape.

np.unravel_index returns a tuple with one element per dimension of the shape. For shape (2,3,4,5), it returns 4 arrays.

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