Data Science Data Wrangling and Preprocessing 2

Question 1

A column of customer ages contains the value 999 for records where age was unknown. What is this an example of?

Accepted Answer

A sentinel value encoding missing data

Answer

Sentinel values like 999 are placeholders that secretly encode missing or unknown data and should be converted to NaN before analysis.

Question 2

In pandas, which method removes rows containing any missing values from a DataFrame?

Accepted Answer

df.dropna()

Answer

dropna() drops rows (or columns) that contain NaN values by default.

Question 3

You merge two tables on a customer ID, but some IDs exist only in the left table. Which join keeps all left-table rows?

Accepted Answer

Left join

Answer

A left join retains every row from the left table and fills unmatched right-table columns with nulls.

Question 4

A date column is stored as strings like '2026-06-30'. What preprocessing step lets you extract the month easily?

Accepted Answer

Convert the column to a datetime type

Answer

Parsing strings into a datetime type exposes attributes like .month, .year, and .dayofweek.

Question 5

Which technique replaces missing numeric values with the column's median?

Accepted Answer

Median imputation

Answer

Median imputation fills NaNs with the median, which is robust to outliers compared to the mean.

Data Science Practice Test