CSV Data Processing and Formatting

Question

Given a CSV-formatted string (header row + data rows, e.g. "col1,col2,col3,col4
value1,value2,value3,value4
"), design and implement a function in pure Python (no external packages such as pandas) that parses and formats the data into a structure usable by other engineering teams. The requirements are intentionally open-ended; the interviewer provides only a single example row and leaves the output format largely up to the candidate. A follow-up then introduces corrupted data: assume headers are always correct, but data rows may have missing values or extra values. The solution must detect and flag invalid rows (e.g. via an is_valid boolean field) so downstream consumers can filter or investigate them.

AceOffer · Accepted Answer

Given a CSV-formatted string (header row + data rows, e.g. "col1,col2,col3,col4
value1,value2,value3,value4
"), design and implement a function in pure Python (no external packages such as pandas) that parses and formats the data into a structure usable by other engineering teams. The requirements are intentionally open-ended; the interviewer provides only a single example row and leaves the output format largely up to the candidate. A follow-up then introduces corrupted data: assume headers are always correct, but data rows may have missing values or extra values. The solution must detect and flag invalid rows (e.g. via an is_valid boolean field) so downstream consumers can filter or investigate them.

Reported follow-ups:
1. How would you handle corrupted data in the CSV? Assume the header row is always correct. (when: After candidate implements clean-data parsing)
2. Can you implement this from scratch using only pure Python, without any external packages? (when: Candidate proposes using pandas or another library)
3. What are the trade-offs of different output formats (column-oriented dict, list-of-dicts, row-index-keyed dict)? (when: Candidate proposes a single output format)

**Alternative approaches:** Column-oriented dict {col_name: [values]} (Simple to construct and easy to access all values for a single column; harder to attach per-row metadata like is_valid.); Row-oriented list of dicts [{col_name: value, ..., is_valid: bool}] (Natural fit for per-row validation flags and easy filtering; slightly more verbose to build.); Row-index-keyed dict {row_index: {col_name: value, is_valid: bool}} (Preserves original row index for debugging corrupted rows; less idiomatic than a list of dicts.)

CSV Data Processing and Formatting

Problem Overview

Follow-up Arc

Approach Trade-offs

Practice

Approach	Notes
Column-oriented dict {col_name: [values]}	Simple to construct and easy to access all values for a single column; harder to attach per-row metadata like is_valid.
Row-oriented list of dicts [{col_name: value, ..., is_valid: bool}]	Natural fit for per-row validation flags and easy filtering; slightly more verbose to build.
Row-index-keyed dict {row_index: {col_name: value, is_valid: bool}}	Preserves original row index for debugging corrupted rows; less idiomatic than a list of dicts.