NaN and None in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does NaN represent in Pandas?

NaN stands for 'Not a Number'. It is used to represent missing or undefined numerical data in Pandas.

beginner

How is None treated in Pandas DataFrames?

None is treated as a missing value in object columns. For numerical columns, it is converted to NaN.

beginner

Which Pandas function can you use to check for missing values including NaN and None?

You can use DataFrame.isna() or DataFrame.isnull() to check for missing values. Both treat NaN and None as missing.

intermediate

What happens when you perform arithmetic operations on NaN values in Pandas?

Any arithmetic operation involving NaN results in NaN. This helps keep track of missing data through calculations.

beginner

How can you replace NaN or None values in a DataFrame?

You can use DataFrame.fillna(value) to replace missing values with a specified value.

What does NaN stand for in Pandas?

A Number zero

B Null and None

C Not a Number

D Negative number

NaN means 'Not a Number' and is used for missing numerical data.

Which function checks for missing values in a Pandas DataFrame?

A DataFrame.fillna()

B DataFrame.isna()

C DataFrame.dropna()

D DataFrame.sum()

DataFrame.isna() returns a boolean mask showing missing values.

How does Pandas treat None in a numeric column?

A As <code>NaN</code>

B As zero

C As a string 'None'

D Raises an error

None is converted to NaN in numeric columns.

What is the result of adding a number to NaN in Pandas?

A The number

B Zero

C An error

D <code>NaN</code>

Any operation with NaN results in NaN.

Which method replaces missing values in a DataFrame?

A fillna()

B isna()

C dropna()

D replace()

fillna() replaces missing values with a specified value.

Explain how Pandas handles NaN and None values in DataFrames.

Think about missing data and how Pandas shows it.

Describe how you can find and replace missing values in a Pandas DataFrame.

Focus on checking and fixing missing data.