Data Science with Python Certification Data Science with Python Data Cleaning and Preparation 2

Question 1

Which pandas method returns a boolean DataFrame indicating where values are missing?

Accepted Answer

df.isna()

Answer

df.isna() (alias: df.isnull()) returns a boolean DataFrame with True where values are NaN.

Question 2

What does the `thresh` parameter in DataFrame.dropna() control?

Accepted Answer

Minimum number of non-NaN values required to keep a row

Answer

thresh=N keeps only rows/columns with at least N non-NaN values.

Question 3

You have a column with values ['1,200', '3,400', '500']. Which approach correctly converts these to integers?

Accepted Answer

df['col'].str.replace(',', '').astype(int)

Answer

You must strip the comma string characters before casting to int since commas are not valid numeric characters.

Question 4

Which scikit-learn class imputes missing values using the median of each feature column?

Accepted Answer

SimpleImputer(strategy='median')

Answer

SimpleImputer with strategy='median' fills NaN values with the column median.

Question 5

What is the primary risk of using forward fill (ffill) to impute time-series data?

Accepted Answer

It propagates stale values across genuinely missing periods

Answer

ffill carries the last valid observation forward, which can mask real gaps and bias analysis if the gap is long.

Data Science with Python Certification Practice Test