Data Science Data Cleaning and Preparation 4

Question 1

In pandas, df['col'].fillna(method='ffill') does what?

Accepted Answer

Fills missing values with the previous valid value

Answer

Fills with the column mean

Answer

Drops missing rows

Answer

Fills with zero

Question 2

Which describes 'data leakage' during preparation?

Accepted Answer

Information from the test set influences training preprocessing

Answer

Saving data to disk too often

Answer

Losing rows when dropping NaNs

Answer

Duplicating a column

Question 3

To avoid leakage, a StandardScaler should be fit on:

Accepted Answer

The training set only, then applied to test data

Answer

The entire dataset before splitting

Answer

The test set only

Answer

Each row independently

Question 4

Binning a continuous 'age' column into groups like 'child', 'adult', 'senior' is called:

Accepted Answer

Discretization

Answer

Normalization

Answer

Imputation

Answer

Deduplication

Question 5

Which is a sign of a poorly structured ('untidy') dataset?

Accepted Answer

Multiple variables stored in a single column

Answer

Each variable in its own column

Answer

Each observation in its own row

Answer

Each value in its own cell

Question 6

Stripping leading/trailing whitespace and fixing inconsistent capitalization in a text column is part of:

Accepted Answer

Text/string cleaning

Answer

Feature scaling

Answer

Cross-validation

Answer

Dimensionality reduction

Question 7

Median imputation is often preferred over mean imputation when the column:

Accepted Answer

Is skewed or has outliers

Answer

Has no missing values

Answer

Is categorical

Answer

Is already scaled