Interpolation for missing numerics in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is interpolation in data analysis?

Interpolation is a method to estimate missing numeric values in data by using existing known values around them.

beginner

Name a common interpolation method used for filling missing numeric data.

Linear interpolation, which estimates missing values by connecting two known points with a straight line.

intermediate

How does pandas' interpolate() function help with missing data?

The pandas interpolate() function fills missing numeric values by estimating them using different methods like linear, time, or polynomial interpolation.

intermediate

Why is interpolation better than simply dropping missing values?

Interpolation keeps the data size intact and preserves trends by estimating missing values, while dropping data can lose important information.

advanced

What is a limitation of interpolation for missing numeric data?

Interpolation assumes that data changes smoothly between points, so it may not work well if data has sudden jumps or is very noisy.

Which method estimates missing values by drawing a straight line between known points?

A Linear interpolation

B Mean imputation

C Mode replacement

D Random sampling

Linear interpolation connects two known points with a straight line to estimate missing values.

What does pandas' interpolate() function do?

A Fills missing numeric values using estimation methods

B Converts numeric data to strings

C Drops rows with missing values

D Sorts data by index

pandas interpolate() fills missing numeric values by estimating them using methods like linear interpolation.

Why might interpolation not work well on some datasets?

A Because it requires categorical data

B Because it assumes smooth changes between points

C Because it deletes data

D Because it only works on text data

Interpolation assumes data changes smoothly, so it may fail if data has sudden jumps or noise.

Which of these is NOT a benefit of interpolation?

A Estimates missing values

B Preserves data trends

C Keeps data size intact

D Removes outliers automatically

Interpolation does not remove outliers; it estimates missing values while keeping data size and trends.

What type of data is interpolation mainly used for?

A Categorical data

B Text data

C Numeric data

D Image data

Interpolation is mainly used to estimate missing numeric data.

Explain what interpolation is and why it is useful for missing numeric data.

Think about how you can guess missing numbers using nearby known numbers.

Describe a situation where interpolation might not be a good choice for filling missing data.

Consider data that changes unpredictably or has sharp spikes.