Problem Description

This problem asks you to transform text content by applying specific capitalization rules. Given a table user_content with columns content_id and content_text, you need to process each text entry according to these rules:

1. Basic Capitalization: Convert the first letter of each word to uppercase and all remaining letters to lowercase. For example, "hello WORLD" becomes "Hello World".

2. Hyphenated Words: Words containing hyphens require special handling. Each part separated by a hyphen should be capitalized independently. For instance:

   • "quick-brown" becomes "Quick-Brown"
   • "modern-day" becomes "Modern-Day"
   • "FRONT-end" becomes "Front-End"

3. Preserve Formatting: All other characters, spacing, and punctuation should remain unchanged.

The solution uses a string processing approach that:

• Splits the text into words by spaces
• For each word, checks if it contains a hyphen
• If hyphenated, splits by hyphen, capitalizes each part, then rejoins with hyphen
• If not hyphenated, simply capitalizes the word normally
• Joins all processed words back together with spaces

The capitalize() method in Python is particularly useful here as it automatically converts the first character to uppercase and the rest to lowercase, handling the basic capitalization requirement perfectly.

The output should include the original text (renamed as original_text), the converted text (as converted_text), and the content_id, demonstrating the transformation applied to each row of text content.

Intuition

The key insight is recognizing that this is a text processing problem with two distinct levels of splitting and transformation. When we look at the examples, we notice a pattern: regular words follow standard title case rules, but hyphenated words need special treatment where each hyphen-separated part is capitalized independently.

The natural approach is to break down the problem hierarchically:

1. First split by spaces to get individual words
2. Then check each word for hyphens and handle accordingly

Why this two-level approach? Consider the text "the QUICK-brown fox". If we tried to handle everything at once, we'd struggle to distinguish between spaces (word separators) and hyphens (sub-word separators). By processing in layers, we maintain the structure of the text.

The beauty of Python's capitalize() method is that it handles both uppercase and lowercase conversions in one operation - it doesn't matter if the input is "HELLO", "hello", or "HeLLo", it always produces "Hello". This eliminates the need for complex case checking logic.

For hyphenated words, we apply the same capitalize() logic to each part. The pattern "-".join([part.capitalize() for part in word.split("-")]) elegantly handles any number of hyphen-separated parts. Even if a word has multiple hyphens like "front-end-development", each part gets capitalized correctly.

The conditional expression if "-" in word acts as our decision point - it's a simple check that routes each word to the appropriate processing path. This branching logic, combined with list comprehension, allows us to process the entire text in a single pass, making the solution both readable and efficient.

Solution Approach

The solution implements a text transformation pipeline using pandas DataFrame operations and custom string processing logic.

Step 1: Define the Text Conversion Function

We create a nested function convert_text that processes each text entry:

def convert_text(text: str) -> str:
    return " ".join(
        (
            "-".join([part.capitalize() for part in word.split("-")])
            if "-" in word
            else word.capitalize()
        )
        for word in text.split(" ")
    )

This function works through several layers:

• text.split(" ") breaks the input text into individual words
• For each word, we check if it contains a hyphen using if "-" in word
• If hyphenated: word.split("-") splits it into parts, part.capitalize() capitalizes each part, and "-".join() reassembles them
• If not hyphenated: word.capitalize() directly applies title case
• Finally, " ".join() combines all processed words back into a single string

Step 2: Apply the Transformation

We use pandas' apply() method to run our conversion function on each row:

user_content["converted_text"] = user_content["content_text"].apply(convert_text)

This creates a new column converted_text with the transformed text while preserving the original data.

Step 3: Format the Output

The final step renames and reorders columns to match the expected output format:

return user_content.rename(columns={"content_text": "original_text"})[
    ["content_id", "original_text", "converted_text"]
]

• rename(columns={"content_text": "original_text"}) changes the column name to match requirements
• Column selection [["content_id", "original_text", "converted_text"]] ensures the correct order

Algorithm Complexity:

• Time: O(n × m) where n is the number of rows and m is the average length of text
• Space: O(n × m) for storing the converted text column

The solution leverages Python's built-in string methods and list comprehensions for clean, efficient text processing, while pandas handles the DataFrame operations seamlessly.

Example Walkthrough

Let's walk through a concrete example to illustrate how the solution processes text with both regular and hyphenated words.

Input:

content_id: 1
content_text: "the QUICK-brown fox JUMPS-OVER the lazy-DOG"

Step-by-Step Processing:

1. Split by spaces: First, we break the text into individual words:

   ["the", "QUICK-brown", "fox", "JUMPS-OVER", "the", "lazy-DOG"]

2. Process each word:

   • "the" → No hyphen detected → Apply capitalize() → "The"

   • "QUICK-brown" → Hyphen detected → Split by "-": ["QUICK", "brown"]

     • "QUICK" → capitalize() → "Quick"
     • "brown" → capitalize() → "Brown"
     • Join with hyphen → "Quick-Brown"

   • "fox" → No hyphen → Apply capitalize() → "Fox"

   • "JUMPS-OVER" → Hyphen detected → Split by "-": ["JUMPS", "OVER"]

     • "JUMPS" → capitalize() → "Jumps"
     • "OVER" → capitalize() → "Over"
     • Join with hyphen → "Jumps-Over"

   • "the" → No hyphen → Apply capitalize() → "The"

   • "lazy-DOG" → Hyphen detected → Split by "-": ["lazy", "DOG"]

     • "lazy" → capitalize() → "Lazy"
     • "DOG" → capitalize() → "Dog"
     • Join with hyphen → "Lazy-Dog"

3. Join all processed words: Combine with spaces:

   "The Quick-Brown Fox Jumps-Over The Lazy-Dog"

Final Output:

content_id: 1
original_text: "the QUICK-brown fox JUMPS-OVER the lazy-DOG"
converted_text: "The Quick-Brown Fox Jumps-Over The Lazy-Dog"

Notice how the capitalize() method handles both uppercase and lowercase inputs uniformly - "QUICK" becomes "Quick" just as "lazy" becomes "Lazy". The hyphen acts as a delimiter that triggers separate capitalization for each part, while spaces are preserved as word boundaries.

Solution Implementation

Time and Space Complexity

Time Complexity: O(n * m * k)

Where:

• n is the number of rows in the DataFrame
• m is the average number of words per content_text entry
• k is the average length of each word

The analysis breaks down as follows:

• Iterating through each row in the DataFrame: O(n)
• For each row, the convert_text function:
  • Splits the text by spaces: O(length of text)
  • For each word in the split result: O(m)
    • Checks if "-" is in the word: O(k)
    • If hyphenated, splits by "-": O(k) and capitalizes each part: O(k * number of parts)
    • If not hyphenated, capitalizes the word: O(k)
    • Joins the parts back with "-": O(k)
  • Joins all words back with spaces: O(m * k)
• DataFrame operations (rename, column selection): O(n)

The dominant operation is processing each word in each row, resulting in O(n * m * k).

Space Complexity: O(n * m * k)

The space breakdown:

• Input DataFrame storage: O(n * m * k) for the original content_text
• The converted_text column: O(n * m * k) for storing the capitalized version
• Temporary space during processing:
  • Split results for each text: O(m * k)
  • List comprehension results: O(m * k)
• Output DataFrame with three columns: O(n * m * k)

The space complexity is dominated by storing the original and converted text columns in the DataFrame, giving us O(n * m * k).

Learn more about how to find time and space complexity quickly.

Common Pitfalls

1. Multiple Consecutive Spaces or Leading/Trailing Spaces

The current implementation using split(" ") doesn't handle multiple consecutive spaces correctly. When there are multiple spaces between words, split(" ") creates empty strings in the resulting list, which leads to incorrect output formatting.

Example Problem:

• Input: "hello world" (two spaces)
• Current output: "Hello World"
• But empty strings from split cause issues: ['hello', '', 'world']

Solution: Use split() without arguments instead of split(" "). This automatically handles multiple spaces and strips leading/trailing whitespace:

def convert_text(text: str) -> str:
    words = text.split()  # Handles multiple spaces automatically
    capitalized_words = []
  
    for word in words:
        if "-" in word:
            parts = word.split("-")
            capitalized_word = "-".join(part.capitalize() for part in parts)
        else:
            capitalized_word = word.capitalize()
        capitalized_words.append(capitalized_word)
  
    return " ".join(capitalized_words)

2. Empty Hyphen Parts

Words with consecutive hyphens or hyphens at the start/end (like "--word", "word-", or "pre--fix") can create empty strings when split by hyphen, causing the capitalize() method to fail or produce unexpected results.

Example Problem:

• Input: "front--end" (double hyphen)
• Split result: ['front', '', 'end']
• capitalize() on empty string returns empty string

Solution: Filter out empty parts or preserve the original hyphen structure:

if "-" in word:
    parts = word.split("-")
    # Only capitalize non-empty parts
    capitalized_parts = [part.capitalize() if part else "" for part in parts]
    capitalized_word = "-".join(capitalized_parts)

3. Special Characters and Numbers

Words starting with numbers or special characters might not behave as expected with capitalize(). For instance, "3rd" remains "3rd" after capitalize(), not "3Rd".

Example Problem:

• Input: "123abc"
• Output: "123abc" (no change, first character is a digit)

Solution: If you need to capitalize the first letter regardless of position:

def smart_capitalize(word):
    if not word:
        return word
    # Find the first letter and capitalize it
    for i, char in enumerate(word):
        if char.isalpha():
            return word[:i] + char.upper() + word[i+1:].lower()
    return word.lower()  # No letters found

4. Unicode and Non-ASCII Characters

The solution might not handle international characters or special Unicode properly, especially in languages with different capitalization rules.

Example Problem:

• Input: "café" or "über"
• Some systems might not capitalize accented characters correctly

Solution: Python's capitalize() generally handles Unicode well, but for specific locale-aware capitalization:

import locale
locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')  # Or appropriate locale

def locale_capitalize(word):
    return word[0].upper() + word[1:].lower() if word else word

5. Performance with Very Long Texts

Creating multiple intermediate lists and strings can be memory-inefficient for very long texts.

Solution: Use generator expressions for better memory efficiency:

def convert_text(text: str) -> str:
    return " ".join(
        "-".join(part.capitalize() for part in word.split("-"))
        if "-" in word else word.capitalize()
        for word in text.split()
    )