How do you convert a non-negative integer (less than 2^31 - 1) into its English words representation?

How do you convert a non-negative integer (less than 2^31 - 1) into its English words representation?

How do you convert a non-negative integer (less than 2^31 - 1) into its English words representation?

Approach

To effectively answer the question of how to convert a non-negative integer (less than \(2^{31} - 1\)) into its English words representation, follow this structured framework:

  1. Understand the Problem: Grasp the requirements of the conversion process.

  2. Break Down the Number: Divide the number into manageable segments (thousands, millions, etc.).

  3. Map Numbers to Words: Create a mapping of numbers to their corresponding English words.

  4. Construct the Final Output: Combine the mapped words into a coherent string that represents the number.

  5. Handle Edge Cases: Consider special cases like zero and large numbers.

Key Points

  • Clarity and Precision: Ensure that each number segment is accurately converted to its word equivalent.

  • Use of Data Structures: Utilize arrays or dictionaries to store the mapping of numbers to words.

  • Efficiency: Aim for a solution that is both time and space-efficient.

  • Testing: Prepare test cases to validate the implementation against expected outputs.

Standard Response

To convert a non-negative integer into its English words representation, we can follow a systematic approach using Python. Below is a sample implementation that adheres to the best practices for clarity, structure, and efficiency:

class Solution:
 def numberToWords(self, num: int) -> str:
 if num == 0:
 return "Zero"
 
 below_twenty = ["", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine",
 "Ten", "Eleven", "Twelve", "Thirteen", "Fourteen", "Fifteen", "Sixteen",
 "Seventeen", "Eighteen", "Nineteen"]
 
 tens = ["", "", "Twenty", "Thirty", "Forty", "Fifty", "Sixty", "Seventy", "Eighty", "Ninety"]
 
 thousands = ["", "Thousand", "Million", "Billion"]
 
 def helper(n):
 if n < 20:
 return below_twenty[n]
 elif n < 100:
 return tens[n // 10] + (" " + below_twenty[n % 10] if n % 10 != 0 else "")
 else:
 return below_twenty[n // 100] + " Hundred" + (" " + helper(n % 100) if n % 100 != 0 else "")
 
 result = ""
 for i, word in enumerate(thousands):
 if num % 1000 != 0:
 result = helper(num % 1000) + (" " + word if word else "") + (" " + result if result else "")
 num //= 1000
 
 return result.strip()

Tips & Variations

Common Mistakes to Avoid

  • Overcomplication: Avoid making the solution overly complex; keep it simple and readable.

  • Ignoring Edge Cases: Ensure to handle cases like 0, which should return "Zero".

  • Misinterpretation of Input Limits: Remember that numbers must be non-negative and less than \(2^{31} - 1\).

Alternative Ways to Answer

  • Recursion vs Iteration: While the provided solution uses recursion, you could also implement it iteratively for those who prefer that style.

  • Using External Libraries: Although not required, you can mention how libraries like inflect in Python could simplify the task.

Role-Specific Variations

  • Technical Roles: Emphasize efficiency and space complexity, discussing the potential for optimization.

  • Managerial Roles: Focus on explaining the process to non-technical stakeholders, highlighting clarity and communication.

  • Creative Roles: Showcase your thought process creatively, perhaps by framing the explanation in a storytelling format.

Follow-Up Questions

  • Can you explain your thought process when you encounter a large number?

  • How would you handle different input types or invalid inputs?

  • What testing strategies would you employ to ensure accuracy?

By structuring your response with this framework, you can convey your thought process clearly and demonstrate your technical skills effectively. This approach not only helps in interviews but also enhances your problem-solving capabilities in real-world scenarios

Question Details

Difficulty
Medium
Medium
Type
Technical
Technical
Companies
Netflix
Amazon
Meta
Netflix
Amazon
Meta
Tags
Programming
Problem-Solving
Attention to Detail
Programming
Problem-Solving
Attention to Detail
Roles
Software Engineer
Data Scientist
Machine Learning Engineer
Software Engineer
Data Scientist
Machine Learning Engineer

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