Data Science with Python Certification Data Science with Python Data Cleaning and Preparation 4

Question 1

Which method removes leading and trailing whitespace from every string in a pandas Series?

Accepted Answer

Series.str.strip()

Answer

The .str accessor exposes vectorized string methods; strip() removes surrounding whitespace.

Question 2

You have 10,000 rows but only 3 rows contain NaN in a non-critical column. What is the most defensible cleaning strategy?

Accepted Answer

Drop those 3 rows since the data loss is negligible

Answer

When missing data is less than 0.1% of rows, dropping them causes negligible bias and avoids imputation assumptions.

Question 3

What is the correct way to rename columns in pandas without modifying the original DataFrame?

Accepted Answer

df.rename(columns={'old': 'new'}, inplace=False)

Answer

rename() with inplace=False (the default) returns a new DataFrame with the renamed columns.

Question 4

Which pandas method converts a wide-format DataFrame to long format?

Accepted Answer

pd.melt()

Answer

pd.melt() unpivots a DataFrame from wide to long format, turning column headers into row values.

Question 5

When standardizing column names for a pipeline, which transformation is considered best practice?

Accepted Answer

Lowercase, replace spaces with underscores, remove special characters

Answer

Snake_case lowercase names are Python-idiomatic, avoid attribute access issues, and work cleanly with SQL and most ML libraries.

Data Science with Python Certification Practice Test