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

Need a 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

Need a 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

Need a 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

Need a hint?

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