Forward fill and backward fill in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is forward fill in data analysis?

Forward fill is a method to fill missing data by copying the last known value forward until a new value appears.

beginner

What does backward fill do in a dataset?

Backward fill fills missing values by copying the next known value backward until a previous value is found.

beginner

How do you perform forward fill on a pandas DataFrame column named 'A'?

Use df['A'].fillna(method='ffill') to fill missing values forward in column 'A'.

intermediate

Why might you choose forward fill over backward fill?

Forward fill is useful when past values logically carry forward, like filling missing sensor readings with the last known measurement.

intermediate

Can forward fill and backward fill be combined? If yes, how?

Yes, you can first apply forward fill to fill missing values, then apply backward fill to fill any remaining missing values at the start of the data.

Which pandas method fills missing values by copying the previous value forward?

A fillna(method='bfill')

B dropna()

C fillna(method='ffill')

D interpolate()

fillna(method='ffill') copies the last known value forward to fill missing data.

What does backward fill do with missing data?

A Copies the previous value forward

B Copies the next value backward

C Drops missing rows

D Replaces missing with zero

Backward fill copies the next known value backward to fill missing data.

If a dataset starts with missing values, which fill method can fill those missing values?

A Backward fill only

B Forward fill only

C Neither forward nor backward fill

D Both forward and backward fill

Backward fill can fill missing values at the start by copying the next known value backward.

Which method is best when missing data should be filled with the last known measurement?

A Forward fill

B Random fill

C Mean imputation

D Backward fill

Forward fill is best when the last known measurement logically carries forward.

What is the pandas syntax to fill missing values backward in a DataFrame column 'B'?

A df['B'].dropna()

B df['B'].fillna(method='ffill')

C df['B'].fillna()

D df['B'].fillna(method='bfill')

fillna(method='bfill') fills missing values by copying the next known value backward.

Explain how forward fill and backward fill work to handle missing data in a dataset.

Think about how missing values can be filled using nearby known values in order.

Describe a real-life example where forward fill would be a better choice than backward fill.

Consider situations where the past value logically continues until updated.