Data Science with Python Certification Data Science with Python Data Cleaning and Preparation 5

Question 1

What is the key difference between `df.fillna(method='ffill')` and `df.interpolate(method='linear')`?

Accepted Answer

ffill repeats the last value while interpolate estimates intermediate values by linear trend

Answer

ffill propagates the last observed value unchanged, while linear interpolation calculates a proportional value between surrounding points.

Question 2

You need to flag rows where ANY column exceeds 3 standard deviations from the mean. Which expression achieves this?

Accepted Answer

((df - df.mean()) / df.std()).abs().gt(3).any(axis=1)

Answer

Computing Z-scores, taking absolute values, checking gt(3), and using any(axis=1) identifies rows with at least one outlier column.

Question 3

Which scikit-learn transformer handles encoding of unknown categories seen at inference but not at training time?

Accepted Answer

Both A and C are valid approaches

Answer

Both OrdinalEncoder with handle_unknown='use_encoded_value' and OneHotEncoder with handle_unknown='ignore' gracefully handle unseen categories.

Question 4

What does the `validate` parameter in `pd.merge()` do?

Accepted Answer

Raises an error if the merge type (one-to-one, one-to-many) does not match expectations

Answer

Setting validate='one_to_one', 'one_to_many', or 'many_to_one' raises a MergeError if the actual relationship differs, catching unexpected duplicates.

Question 5

When splitting data into train and test sets, why must any imputation be fit ONLY on the training set?

Accepted Answer

To prevent data leakage where test statistics influence training preprocessing

Answer

Fitting on all data before splitting leaks test set statistics into training, producing overly optimistic performance estimates.

Data Science with Python Certification Practice Test

Data Science with Python Certification Practice Test

Data Science with Python Certification Data Science with Python Data Cleaning and Preparation 5