🦄 Mini CSV: Sum Column by Header

Difficulty: Intermediate Tags: parsing, strings, data-processing Series: CS 101

Problem

In the mystical archives, scrolls are written in CSV (Comma-Separated Values) format. You must parse these scrolls and extract numerical insights from specific columns.

Given a CSV string where: - The first line contains column headers - Subsequent lines contain data rows - Fields are separated by commas

Return the sum of all values in the column specified by header.

Real-World Application

CSV parsing appears everywhere in data processing: - Data analysis - reading datasets for processing (pandas alternative) - ETL pipelines - extracting data from exports and reports - Log analysis - parsing structured log files - API integrations - processing CSV responses from web services - Spreadsheet automation - reading Excel/Google Sheets exports - Database imports - bulk data loading

Input

data = {
    'csv': str,     # CSV string with headers and data
    'header': str   # Column name to sum
}

Output

int  # Sum of all values in the specified column

Constraints

• Lines separated by \n (newline)
• Fields separated by , (comma)
• Trim whitespace around field values
• Header is guaranteed to exist
• Non-integer values (empty, text, etc.) treated as 0
• Ignore empty lines

Examples

Example 1: Basic CSV

Input:

{
    'csv': 'name,score\nA,10\nB,20',
    'header': 'score'
}

Output: 30

Explanation: - Headers: ['name', 'score'] - Row 1: A has score 10 - Row 2: B has score 20 - Sum of 'score' column: 10 + 20 = 30

Example 2: CSV with Spaces

Input:

{
    'csv': 'name, score\nA, 7\nB, 8',
    'header': 'score'
}

Output: 15

Explanation: - Headers after trimming: ['name', 'score'] - Row 1: score = 7 (trimmed from ' 7') - Row 2: score = 8 (trimmed from ' 8') - Sum: 7 + 8 = 15

Example 3: Missing Values and Blank Lines

Input:

{
    'csv': 'name,score\nA,\n\nC,5',
    'header': 'score'
}

Output: 5

Explanation: - Row 1: A has empty score → treated as 0 - Row 2: Empty line → ignored - Row 3: C has score 5 - Sum: 0 + 5 = 5

Example 4: Multiple Columns

Input:

{
    'csv': 'name,age,score\nAlice,25,100\nBob,30,85\nCarol,28,92',
    'header': 'age'
}

Output: 83

Explanation: - Sum of 'age' column: 25 + 30 + 28 = 83

Example 5: Non-Numeric Values

Input:

{
    'csv': 'name,score\nAlice,100\nBob,invalid\nCarol,50',
    'header': 'score'
}

Output: 150

Explanation: - Alice: 100 - Bob: 'invalid' → treated as 0 - Carol: 50 - Sum: 100 + 0 + 50 = 150

What You'll Learn

• How to parse delimited text data
• String manipulation and trimming techniques
• Handling edge cases (empty values, blank lines)
• Mapping column names to indices
• Robust error handling for data parsing

Why This Matters

CSV parsing is a fundamental skill for data engineering. Interview questions test your ability to handle malformed data, edge cases, and write robust parsing logic without relying on libraries.

Starter Code

def challenge_function(data):
    """
    Sum all values in a specified CSV column.

    Args:
        data: dict with keys:
            - 'csv' (str): CSV string with headers and data
            - 'header' (str): Column name to sum

    Returns:
        int: Sum of all numeric values in the column
    """
    # Your implementation here
    pass