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Session 7 Part 2 quiz

Question 1

What does movies.isnull().sum() show?

  • The total number of rows.
  • Missing values per column.
  • Duplicate rows only.
  • The average value of each numeric column.

Answer: 2 Type: single Time: 45 Explanation: isnull() marks missing values and sum() counts them by column.

Question 2

Why might you use movies.copy() before cleaning?

  • To test cleaning steps without changing the original DataFrame.
  • To delete the original DataFrame.
  • To make pandas run without importing it.
  • To convert a CSV file into JSON.

Answer: 1 Type: single Time: 45 Explanation: A copy lets you experiment while preserving the raw data.

Question 3

Which method can fill missing values?

  • fillna()
  • findna()
  • replace_columns()
  • missing()

Answer: 1 Type: single Time: 45 Explanation: fillna() replaces missing values with a chosen value.

Question 4

What does value_counts(dropna=False) do?

  • Counts values and includes missing values in the count.
  • Deletes missing values before counting.
  • Converts all values to numbers.
  • Shows only the first five values.

Answer: 1 Type: single Time: 60 Explanation: dropna=False keeps missing values visible in the count.

Question 5

Which statement is the safest?

  • Always replace missing numeric values with zero.
  • Always delete rows with missing values.
  • Decide how to handle missing values based on the column and analysis goal.
  • Ignore all missing values.

Answer: 3 Type: single Time: 60 Explanation: Missing-value handling is a data decision, not an automatic rule.

Question 6

What is one risk of filling missing ratings with the mean?

  • It can hide missingness and reduce variation in the data.
  • It changes the column name.
  • It makes the DataFrame impossible to print.
  • It creates duplicate column names.

Answer: 1 Type: single Time: 60 Explanation: Mean filling can make the data look cleaner than it really is.

Question 7

What does interpolation do?

  • Estimates missing numeric values between known values.
  • Deletes all missing rows.
  • Converts text columns to uppercase.
  • Counts duplicate rows.

Answer: 1 Type: single Time: 60 Explanation: Interpolation estimates missing numeric values using surrounding values.

Question 8

When is interpolation most likely to make sense?

  • When rows have a meaningful order, such as weeks or time.
  • When filling a movie genre column.
  • When renaming columns.
  • When counting unique values.

Answer: 1 Type: single Time: 60 Explanation: Interpolation depends on order, so it is most suitable for ordered numeric data.