Missing data strategies decision in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the simplest way to handle missing data in a dataset?

The simplest way is to remove rows or columns that contain missing values using methods like dropna() in pandas.

beginner

What does 'imputation' mean in the context of missing data?

Imputation means filling in missing values with estimated ones, such as the mean, median, or a constant value.

intermediate

When should you prefer imputing missing data instead of dropping it?

Imputation is preferred when dropping data would cause loss of too much information or bias the results.

beginner

What pandas function can you use to fill missing values with the column mean?

You can use df['column'].fillna(df['column'].mean()) to replace missing values in a column with its mean.

intermediate

Name one risk of using mean imputation for missing data.

Mean imputation can reduce data variability and may bias the analysis if data is not missing at random.

Which pandas method removes rows with missing values?

A replace()

B fillna()

C isnull()

D dropna()

dropna() removes rows or columns with missing values.

What is a common strategy to fill missing numeric data?

A Remove all data

B Fill with string 'missing'

C Fill with mean

D Fill with random text

Filling missing numeric data with the mean is a common imputation method.

When is dropping missing data NOT a good idea?

A When missing data is rare

B When missing data is large and important

C When data is complete

D When data is categorical

Dropping data is bad if it causes loss of important information.

Which pandas method fills missing values with a specified value?

A fillna()

B dropna()

C isnull()

D notnull()

fillna() replaces missing values with a given value.

What is a potential downside of mean imputation?

A Reduces data variability and may bias results

B Removes all missing data

C Increases data variability

D Creates new missing values

Mean imputation reduces variability and can bias analysis.

Explain the main strategies to handle missing data and when to use each.

Think about removing vs filling missing values and their effects.

Describe how to use pandas to fill missing values with the mean of a column.

Focus on pandas functions for imputation.