Partial sorting with np.partition() in NumPy - Mini Project: Build & Apply

Partial Sorting with np.partition()

📖 Scenario: You work in a sports analytics company. You have a list of players' scores from a recent tournament. You want to quickly find the top 3 scores without sorting the entire list.

🎯 Goal: Use np.partition() to find the top 3 scores from the players' scores list efficiently.

📋 What You'll Learn

Create a numpy array called scores with the exact values given.

Create a variable called k to represent the number of top scores to find.

Use np.partition() to partially sort the array and find the top k scores.

Print the top k scores in ascending order.

💡 Why This Matters

🌍 Real World

Partial sorting is useful when you only need the top or bottom few values from large datasets, like finding top players, best sales, or highest ratings quickly.

💼 Career

Data scientists and analysts often use partial sorting to speed up data processing and focus on important subsets without sorting entire datasets.

1

Create the scores array

Create a numpy array called scores with these exact values: [50, 20, 90, 30, 70, 10, 80].

NumPy

import numpy as np
# Create the numpy array called scores with the given values
# Your code here

Hint

Use np.array() to create the array with the exact list of scores.

2

Set the number of top scores to find

Create a variable called k and set it to 3 to represent the top 3 scores you want to find.

NumPy

import numpy as np
scores = np.array([50, 20, 90, 30, 70, 10, 80])
# Create the variable k and set it to 3
# Your code here

Hint

Just assign the number 3 to the variable k.

3

Use np.partition() to find the top k scores

Use np.partition() on scores with len(scores) - k to partially sort and find the top k scores. Store the result in partitioned_scores. Then extract the top k scores from partitioned_scores[len(scores) - k:] and sort them in ascending order using np.sort(). Store this sorted array in top_k_scores.

NumPy

import numpy as np
scores = np.array([50, 20, 90, 30, 70, 10, 80])
k = 3
# Use np.partition() to partially sort scores and find top k scores
# Your code here

Hint

Use np.partition(scores, len(scores) - k) to get the array partially sorted so that the top k scores are at the end. Then sort those last k scores with np.sort().

4

Print the top k scores

Print the variable top_k_scores to display the top 3 scores in ascending order.

NumPy

import numpy as np
scores = np.array([50, 20, 90, 30, 70, 10, 80])
k = 3
partitioned_scores = np.partition(scores, len(scores) - k)
top_k_scores = np.sort(partitioned_scores[len(scores) - k:])
# Print the top_k_scores
# Your code here

Hint

Use print(top_k_scores) to show the final top 3 scores.

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