LeetCode 595: Big Countries Solution in Python – A Step-by-Step Guide

What Is LeetCode 595: Big Countries?

In LeetCode 595: Big Countries, you’re given a list of tuples countries where each tuple (name, area, population) represents a country’s name, area in square kilometers, and population in millions. Your task is to return a list of names of countries classified as “big”—those with an area of at least 3,000,000 km² or a population of at least 25,000,000. For example, with countries = [("USA", 9833517, 331002651), ("Canada", 9984670, 37742154), ("Vatican", 0.44, 800)], the result is ["USA", "Canada"] because both exceed the thresholds, while Vatican does not. This problem builds on LeetCode 349: Intersection of Two Arrays for filtering but focuses on simple condition-based selection from structured data.

Problem Statement

• Input: countries (List[Tuple[str, float, int]])—list of (name, area, population).
• Output: List[str]—names of countries where area ≥ 3,000,000 km² or population ≥ 25,000,000.
• Rules: Filter based on area or population thresholds; return names only.

Constraints

• 1 <= countries.length <= 10⁴
• 0 <= area <= 10⁷ (in km²)
• 0 <= population <= 2 * 10⁹
• Names are strings of length 1 to 50

Examples

• Input: countries = [("USA",9833517,331002651),("Canada",9984670,37742154),("Vatican",0.44,800)]
• Output: ["USA","Canada"]
• USA: area > 3M, pop > 25M; Canada: area > 3M, pop > 25M; Vatican: neither.
• Input: countries = [("Monaco",2.02,39242)]
• Output: []
• Area < 3M, pop < 25M.
• Input: countries = [("Russia",17125191,145912025)]
• Output: ["Russia"]
• Area > 3M, pop > 25M.

Understanding the Problem: Filtering Big Countries

To solve LeetCode 595: Big Countries in Python, we need to filter a list of country records to extract names where the area is at least 3 million km² or the population is at least 25 million, returning them as a list, handling up to 10⁴ records efficiently. A brute-force approach scanning the list repeatedly for each condition could work but is unnecessary since a single pass suffices. Instead, a list comprehension processes the list in O(n) time, leveraging Python’s concise syntax for filtering. We’ll explore:

• Best Solution (List Comprehension with Filtering): O(n) time, O(n) space—fast and optimal (n = number of countries).
• Alternative Solution (Brute-Force Traversal): O(n) time, O(1) space—thorough but redundant for this simplicity.

Let’s dive into the list comprehension solution with a friendly breakdown!

Best Solution: List Comprehension with Filtering

Why List Comprehension Wins

The list comprehension with filtering solution is the best for LeetCode 595 because it identifies big countries in O(n) time and O(n) space by processing the list in a single pass, using a concise condition to check area or population thresholds, and collecting qualifying names directly into a list. It’s like flipping through a country catalog, jotting down the big ones as you go—all in a quick, elegant sweep!

How It Works

Think of this as a country-sifting organizer:

• Step 1: Define Condition:
• Big country: area >= 3000000 or population >= 25000000.
• Step 2: Filter with List Comprehension:
• Iterate countries, unpack tuple, apply condition, collect names.
• Step 3: Return Result:
• List of big country names.
• Why It Works:
• Single pass ensures efficiency.
• List comprehension leverages Python’s readability.

It’s like a big-country spotting maestro!

Step-by-Step Example

Example: countries = [("USA",9833517,331002651),("Canada",9984670,37742154),("Vatican",0.44,800)]

• Step 1: Condition: area >= 3000000 or population >= 25000000.
• Step 2: Filter:
• ("USA", 9833517, 331002651):
• 9833517 > 3000000 (True), 331002651 > 25000000 (True), include "USA".
• ("Canada", 9984670, 37742154):
• 9984670 > 3000000 (True), 37742154 > 25000000 (True), include "Canada".
• ("Vatican", 0.44, 800):
• 0.44 < 3000000 (False), 800 < 25000000 (False), skip.
• Step 3: Result: ["USA", "Canada"].
• Result: ["USA", "Canada"].

Example: countries = [("Monaco",2.02,39242)]

• Step 1: Condition check:
• 2.02 < 3000000 (False), 39242 < 25000000 (False), skip.
• Step 2: Result: [].
• Result: [].

Code with Detailed Line-by-Line Explanation

Here’s the Python code, explained for beginners:

Copy codefrom typing import List, Tuple

class Solution:
    def bigCountries(self, countries: List[Tuple[str, float, int]]) -> List[str]:
        # Step 1: Use list comprehension to filter big countries
        return [name for name, area, population in countries 
                if area >= 3000000 or population >= 25000000]

• Line 6-7: List comprehension:
• Unpack each tuple into name, area, population.
• Condition: area >= 3000000 or population >= 25000000.
• Collect name if condition is true.
• Time Complexity: O(n)—single pass over countries.
• Space Complexity: O(n)—result list storage.

It’s like a country-filtering wizard!

Alternative Solution: Brute-Force Traversal

Why an Alternative Approach?

The brute-force traversal approach scans the country list once but mimics a less optimized mindset by explicitly iterating and checking conditions without leveraging Python’s concise features, running in O(n) time and O(1) space (excluding output). It’s thorough but redundant compared to the list comprehension, making it a good baseline for understanding or when avoiding advanced constructs.

How It Works

Picture this as a country-scanning seeker:

• Step 1: Initialize empty result list.
• Step 2: Iterate countries, check area/population conditions.
• Step 3: Add qualifying names to result.
• Step 4: Return result list.

It’s like a manual country checker!

Step-by-Step Example

Example: countries = [("USA",9833517,331002651),("Vatican",0.44,800)]

• Step 1: result = [].
• Step 2: Iterate:
• ("USA", 9833517, 331002651): 9833517 > 3000000 (True), add "USA".
• ("Vatican", 0.44, 800): 0.44 < 3000000, 800 < 25000000, skip.
• Step 3: Result: ["USA"].
• Result: ["USA"].

Code for Brute-Force Approach

Copy codefrom typing import List, Tuple

class Solution:
    def bigCountries(self, countries: List[Tuple[str, float, int]]) -> List[str]:
        # Step 1: Initialize result list
        result = []

        # Step 2: Traverse and filter
        for name, area, population in countries:
            if area >= 3000000 or population >= 25000000:
                result.append(name)

        # Step 3: Return result
        return result

• Line 7: Empty list for results.
• Lines 10-12: Check condition, append name if satisfied.
• Line 15: Return list.
• Time Complexity: O(n)—single pass.
• Space Complexity: O(1)—minimal space (excluding output).

It’s a brute-force country sifter!

Comparing the Two Solutions

• List Comprehension (Best):
• Pros: O(n), O(n), concise and elegant.
• Cons: Slightly less explicit for beginners.
• Brute-Force (Alternative):
• Pros: O(n), O(1), simple explicit logic.
• Cons: Less Pythonic, redundant for this task.

List comprehension wins for elegance!

Additional Examples and Edge Cases

• Empty list: [].
• All small: [].
• Mixed sizes: Correct filtering.

List comprehension handles them all!

Complexity Recap

• List Comprehension: Time O(n), Space O(n).
• Brute-Force: Time O(n), Space O(1).

List comprehension’s the clarity champ!

Key Takeaways

• List Comprehension: Filter finesse—learn at Python Basics!
• Brute-Force: Scan simplicity.
• Countries: Filtering is fun.
• Python: List comp or loops, your pick!

Final Thoughts: Spot Those Big Countries!

LeetCode 595: Big Countries in Python is a straightforward data challenge. List comprehension with filtering is your fast track, while brute-force offers a thorough dive. Want more? Try LeetCode 349: Intersection of Two Arrays or LeetCode 217: Contains Duplicate. Ready to sift? Head to Solve LeetCode 595 on LeetCode and identify those big countries today!