Dropping missing values (dropna) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the dropna() function do in data analysis?

The dropna() function removes rows or columns that contain missing values (NaN) from a dataset, helping to clean the data for analysis.

beginner

How can you use dropna() to remove columns with missing values instead of rows?

You can use dropna(axis=1) to remove columns that have any missing values instead of rows.

intermediate

What does the parameter how='all' do in dropna()?

The parameter how='all' tells dropna() to drop only those rows or columns where all values are missing.

intermediate

What is the effect of dropna(thresh=2) on a DataFrame?

It keeps only rows or columns with at least 2 non-missing values, dropping those with fewer than 2 valid entries.

beginner

Why is it important to drop missing values before analysis?

Missing values can cause errors or misleading results in calculations and models, so dropping them helps ensure accurate and reliable analysis.

What does df.dropna() do by default?

A Fills missing values with zero

B Drops columns with any missing values

C Drops rows with any missing values

D Keeps all rows and columns

dropna() removes rows that have at least one missing value by default.

How do you drop columns with missing values using dropna()?

A df.dropna(thresh=1)

B df.dropna(axis=0)

C df.dropna(how='all')

D df.dropna(axis=1)

Setting axis=1 tells dropna() to drop columns instead of rows.

What does dropna(how='all') do?

A Drops rows or columns where all values are missing

B Drops rows or columns with any missing value

C Drops no rows or columns

D Fills missing values with the mean

The how='all' option drops only rows or columns where every value is missing.

What does the thresh parameter control in dropna()?

A Minimum number of non-missing values required to keep the row/column

B Maximum number of missing values allowed

C Whether to drop rows or columns

D The value to replace missing data

thresh sets the minimum count of non-NaN values needed to keep a row or column.

Why might you choose to drop missing values instead of filling them?

A Because missing values are always errors

B To avoid introducing bias from incorrect guesses

C Because filling is always slower

D To increase dataset size

Dropping missing values avoids adding potentially wrong data that could bias results.

Explain how to use dropna() to clean a dataset by removing rows or columns with missing values.

Think about the axis and how parameters.

Describe why handling missing data is important before doing data analysis.

Consider the impact of missing values on results.