Filling missing values with fillna() in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the fillna() function do in pandas?

The fillna() function replaces missing values (NaN) in a DataFrame or Series with a specified value or method.

beginner

How can you fill missing values with the mean of a column using fillna()?

Calculate the mean of the column and pass it to fillna(). For example: df['col'].fillna(df['col'].mean(), inplace=True).

intermediate

What does the parameter method='ffill' do in fillna()?

It fills missing values by propagating the last valid observation forward to the next missing value.

intermediate

Can fillna() fill missing values differently for each column in a DataFrame?

Yes, by passing a dictionary with column names as keys and fill values as values, e.g., df.fillna({'A': 0, 'B': 1}).

beginner

What happens if you use fillna() without inplace=True?

It returns a new DataFrame or Series with missing values filled, but does not change the original data unless you assign it back or use inplace=True.

What is the default behavior of fillna() if no value or method is specified?

A It does nothing and returns the original data

B It fills missing values with zero

C It raises an error

D It fills missing values with the mean

If no value or method is specified, fillna() returns the original data unchanged.

Which fillna() method fills missing values using the next valid value?

A method='ffill'

B value='mean'

C value=0

D method='bfill'

method='bfill' fills missing values by using the next valid value (backward fill).

How do you fill missing values in multiple columns with different values using fillna()?

A Pass a list of values

B Pass a dictionary with column names and values

C Call <code>fillna()</code> multiple times

D Use <code>method='ffill'</code>

Passing a dictionary like {'col1': val1, 'col2': val2} fills each column with its specified value.

What does inplace=True do in fillna()?

A Deletes missing values

B Returns a copy with filled values

C Modifies the original data directly

D Raises an error if missing values exist

inplace=True changes the original DataFrame or Series without needing assignment.

Which of these is NOT a valid way to fill missing values with fillna()?

A Using <code>fillna()</code> without any arguments

B Using a method like 'ffill'

C Using a dictionary for different columns

D Using a scalar value like 0

fillna() without arguments does not fill missing values and returns data unchanged.

Explain how you would fill missing values in a DataFrame column with the column's median using fillna().

Think about how to get the median and pass it to fillna.

Describe the difference between using method='ffill' and method='bfill' in fillna().

Consider the direction of filling missing values.