Filling missing values with fillna() in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to fill missing values in the DataFrame column 'Age' with 30.

Pandas

df['Age'] = df['Age'].[1](30)

Drag options to blanks, or click blank then click option'

Afillna

Bisnull

Cdropna

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using replace() instead of fillna() does not specifically target missing values.

Using dropna() removes rows with missing values instead of filling them.

The fillna() method replaces missing values with the specified value, here 30.

2fill in blank

medium

Complete the code to fill missing values in the entire DataFrame with the value 0.

Pandas

df = df.[1](0)

Drag options to blanks, or click blank then click option'

Adropna

Bfillna

Creplace

Disnull

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() removes rows with missing values instead of filling them.

Using replace() does not specifically target missing values.

The fillna() method fills all missing values in the DataFrame with 0.

3fill in blank

hard

Fix the error in the code to fill missing values in column 'Score' with the mean of that column.

Pandas

df['Score'] = df['Score'].[1](df['Score'].mean())

Drag options to blanks, or click blank then click option'

Afillna

Bisnull

Cdropna

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using replace() does not fill missing values correctly.

Using dropna() removes rows instead of filling missing values.

Use fillna() to replace missing values with the mean of the column.

4fill in blank

hard

Fill both blanks to create a dictionary that fills missing values in 'Height' with 170.

Pandas

df = df.fillna([1]: [2])

Drag options to blanks, or click blank then click option'

A'Height'

B170

C65

D'Weight'

Attempts:

3 left

💡 Hint

Common Mistakes

Using numbers as keys instead of column names.

Swapping keys and values.

The dictionary key is the column name as a string, and the value is the fill value.

5fill in blank

hard

Fill all three blanks to create a dictionary that fills missing values in 'Height' with 170 and 'Weight' with 65, then apply it with fillna().

Pandas

fill_values = [1]: [2], [3]: 65}
df = df.fillna(fill_values)

Drag options to blanks, or click blank then click option'

A'Height'

B170

C'Weight'

D65

Attempts:

3 left

💡 Hint

Common Mistakes

Not quoting column names.

Using strings for numeric fill values.

The dictionary keys are column names as strings, and values are the fill values.