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Python Dictionary Cleaning Theory and Coding Quiz

Question 1

Why is csv.DictReader often easier to maintain than csv.reader?

  • It skips rows with missing values automatically
  • It allows access by column name instead of numeric index
  • It always uses less memory than csv.reader
  • It converts strings into numbers automatically

Answer: 2 Type: single Time: 40 Explanation: Column-name access makes code clearer and reduces index mistakes.

Question 2

Given movie = {"music_by": ""}, which check correctly treats the field as missing?

  • if movie["music_by"] is None:
  • if movie["music_by"] == 0:
  • if movie["music_by"].strip() == "":
  • if len(movie["music_by"]) > 0:

Answer: 3 Type: single Time: 40 Explanation: In CSV-style data cleaning, missing values are often empty strings, so stripping and comparing to "" is reliable.

Question 3

Which pattern best describes a basic cleaning workflow?

  • Read -> train model -> deploy
  • Detect missing values -> update/fix -> validate -> save
  • Sort -> reverse -> deduplicate only
  • Compress -> encrypt -> upload

Answer: 2 Type: single Time: 40 Explanation: Practical cleaning typically includes detection, correction, verification, and writing cleaned output.

Question 4

What does this line do?

movie["music_by"] = "Joe Hisaishi"

  • Deletes a key from the dictionary
  • Creates or updates the music_by value
  • Converts the whole dictionary to CSV
  • Appends a new dictionary to a list

Answer: 2 Type: single Time: 35 Explanation: Dictionary assignment updates an existing key or creates it if missing.

Question 5

When writing cleaned rows to CSV with DictWriter, which steps are required?

  • Create DictWriter with fieldnames
  • Call writeheader()
  • Call writerows(rows)
  • Use json.dump in the same file handle

Answer: 1,2,3 Type: multiple Time: 45 Explanation: DictWriter needs field names, then header writing, then row writing. json.dump is unrelated to CSV output.

Question 6

If your script loops through all n rows once to detect missing values, what is the time complexity?

  • O(1)
  • O(log n)
  • O(n)
  • O(n^2)

Answer: 3 Type: single Time: 35 Explanation: A single full pass over rows is linear time.

Question 7

If your script stores all cleaned rows in a list before saving, what is the extra space complexity with respect to rows?

  • O(1)
  • O(log n)
  • O(n)
  • O(n^2)

Answer: 3 Type: single Time: 35 Explanation: Keeping all rows in memory grows linearly with dataset size.

Question 8

Why is saving cleaned data to a new file often better than overwriting the raw input immediately?

  • It is always faster
  • It preserves the original data for audit and rollback
  • It removes the need for validation
  • It guarantees perfect cleaning

Answer: 2 Type: single Time: 40 Explanation: Keeping raw data intact makes debugging and verification safer.

Question 9

Which statements about dictionary/list cleaning code are true?

  • for row in rows: iterates one dictionary per row
  • row["year"] = "1989" updates the current row
  • break can stop at the first match
  • int(row["year"]) is always safe without checks

Answer: 1,2,3 Type: multiple Time: 45 Explanation: The first three are core loop behaviors; converting to int is unsafe if values are missing or non-numeric.

Question 10

Which strategy is best before computing an average from CSV text values?

  • Convert every value with int() directly without checks
  • Skip conversion and average strings
  • Validate/clean values first, then convert numeric fields
  • Sort rows alphabetically first

Answer: 3 Type: single Time: 40 Explanation: Numeric statistics should be computed only after missing/invalid values are handled.