Partial sorting with np.partition() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to partially sort the array so the smallest 3 elements are in the first 3 positions.

NumPy

import numpy as np
arr = np.array([7, 2, 5, 3, 9, 1])
result = np.partition(arr, [1])
print(result)

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

A3

B2

C4

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using an index larger than the array length causes an error.

Confusing the partition index with sorting the whole array.

The second argument to np.partition is the index where the array is partitioned. Using 3 puts the smallest 3 elements in the first 3 positions.

2fill in blank

medium

Complete the code to get the 2 smallest elements using np.partition.

NumPy

import numpy as np
arr = np.array([10, 4, 6, 2, 8])
partitioned = np.partition(arr, [1])
smallest_two = partitioned[:2]
print(smallest_two)

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A2

B1

C3

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 2 as partition index returns 3 smallest elements, not 2.

Using 0 returns only the smallest element.

Using np.partition(arr, 1) places the smallest 2 elements in the first two positions (indices 0 and 1).

3fill in blank

hard

Fix the error in the code to correctly partition the array to get the 4 smallest elements.

NumPy

import numpy as np
arr = np.array([9, 1, 7, 3, 5])
result = np.partition(arr, [1])
print(result[:4])

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A3

B4

C2

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using 4 as partition index causes an error because index is out of bounds.

Using 5 is invalid since array length is 5 and max index is 4.

The partition index should be 3 to place the 4 smallest elements in the first 4 positions (indices 0 to 3).

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 4 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
lengths = { [1] : [2] for word in words if len(word) > 4 }
print(lengths)

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Aword

Blen(word)

Cword.upper()

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using len as a value instead of len(word).

Using word.upper() as key changes the original word.

The dictionary comprehension uses the word as key and its length as value for words longer than 4 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, but only for words shorter than 6 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
result = { [1] : [2] for word in words if len(word) [3] 5 }
print(result)

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

Aword.upper()

Blen(word)

C<=

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' instead of '<=' in the condition.

Using 'word' instead of 'word.upper()' as keys.

The dictionary comprehension uses uppercase words as keys and their lengths as values for words with length less than or equal to 5.

Practice

1. What does np.partition do to an array?

easy

A. It rearranges the array so the kth element is in its sorted position, with partial order around it.

B. It fully sorts the entire array in ascending order.

C. It reverses the array elements.

D. It removes duplicate elements from the array.

Solution

1. Step 1: Understand np.partition behavior

   np.partition places the kth smallest element in its correct sorted position.

2. Step 2: Recognize partial ordering

   Elements before the kth are smaller or equal, and elements after are larger or equal, but not fully sorted.

3. Final Answer:

   It rearranges the array so the kth element is in its sorted position, with partial order around it. -> Option A

4. Quick Check:

   Partial sorting = kth element fixed [OK]

Hint: Remember: np.partition fixes kth element only [OK]

Common Mistakes:

• Thinking np.partition fully sorts the array
• Assuming it reverses or removes duplicates
• Confusing np.partition with np.sort

2. Which of the following is the correct syntax to partition array arr at index 3 using np.partition?

easy

A. np.partition(arr, 3)

B. np.partition(3, arr)

C. arr.partition(3)

D. np.partition(arr, k=3)

Solution

1. Step 1: Check np.partition function signature

   The function is called as np.partition(array, kth), where kth is the index.

2. Step 2: Match syntax with options

   np.partition(arr, 3) matches the correct order: array first, then kth index.

3. Final Answer:

   np.partition(arr, 3) -> Option A

4. Quick Check:

   np.partition(array, kth) syntax [OK]

Hint: Remember: array first, kth second in np.partition() [OK]

Common Mistakes:

• Swapping arguments order
• Using method call on array (arr.partition)
• Using incorrect keyword argument like k=3

3. What is the output of the following code?

import numpy as np
arr = np.array([7, 2, 5, 3, 9])
result = np.partition(arr, 2)
print(arr)

medium

A. [2 3 5 7 9]

B. [7 2 5 3 9]

C. [2 3 5 7 9] sorted

D. [5 2 3 7 9]

Solution

1. Step 1: Identify kth element and partial sorting

   kth=2 means the element at index 2 in sorted order is placed correctly. The 3rd smallest element is 5.

2. Step 2: Rearrange array with partial order

   Elements before index 2 are smaller or equal to 5, after are larger or equal. The output is [7 2 5 3 9] because np.partition returns a new array and does not modify arr in place.

3. Final Answer:

   [7 2 5 3 9] -> Option B

4. Quick Check:

   np.partition returns a new array, original unchanged [OK]

Hint: Check kth element position, others partially ordered [OK]

Common Mistakes:

• Expecting fully sorted output
• Confusing kth index with value
• Ignoring partial order after kth

4. The code below throws an error. What is the mistake?

import numpy as np
arr = np.array([4, 1, 6, 8])
result = np.partition(arr, '2')
print(result)

medium

A. The array must be sorted before partitioning.

B. np.partition does not accept arrays as input.

C. np.partition requires a keyword argument kth=2.

D. The kth argument should be an integer, not a string.

Solution

1. Step 1: Check argument types for np.partition

   The kth argument must be an integer index, not a string.

2. Step 2: Identify error cause

   Passing '2' (string) causes a TypeError; correct is integer 2.

3. Final Answer:

   The kth argument should be an integer, not a string. -> Option D

4. Quick Check:

   kth must be int, not str [OK]

Hint: kth index must be int, not string [OK]

Common Mistakes:

• Passing kth as string instead of int
• Thinking array must be sorted first
• Using keyword argument kth which is invalid

5. You have a large dataset array data with 1 million numbers. You want to quickly find the 1000 smallest values without fully sorting. Which code snippet using np.partition is best?

hard

A. np.partition(data, 1000)[:1000]

B. np.sort(data)[:1000]

C. np.partition(data, 999)[:1000]

D. np.partition(data, -1000)[-1000:]

Solution

1. Step 1: Understand kth index for 1000 smallest

   Indices start at 0, so the 1000th smallest is at index 999.

2. Step 2: Use np.partition to get partial sorted array

   Partition at 999 puts 1000 smallest elements before index 999, so slicing [:1000] gets them.

3. Step 3: Check other options

   np.partition(data, 1000)[:1000] partitions at 1000 (off by one), C fully sorts (slow), D uses negative index (wrong for smallest).

4. Final Answer:

   np.partition(data, 999)[:1000] -> Option C

5. Quick Check:

   kth=999 for 1000 smallest [OK]

Hint: Use kth = count-1 for smallest elements [OK]

Common Mistakes:

• Using kth = 1000 instead of 999
• Using full sort instead of partition
• Using negative kth for smallest values