Detecting missing values with isna() in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check for missing values in the DataFrame column 'Age'.

Pandas

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

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

Aisna

Bnotna

Cisnull

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() which fills missing values instead of detecting them.

Using notna() which detects non-missing values.

The isna() function returns a boolean Series showing which values are missing (NaN).

2fill in blank

medium

Complete the code to count how many missing values are in the 'Salary' column.

Pandas

missing_count = df['Salary'].[1]().sum()

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

Adropna

Bfillna

Cnotna

Disna

Attempts:

3 left

💡 Hint

Common Mistakes

Using notna() which returns True for non-missing values.

Using dropna() which removes missing values instead of detecting them.

isna() returns True for missing values, and summing counts them.

3fill in blank

hard

Fix the error in the code to correctly detect missing values in the DataFrame 'df'.

Pandas

missing_data = df.[1]()

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

Afillna

Bdropna

Cisna

Dnotna

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() which fills missing values instead of detecting them.

Using dropna() which removes missing values.

isna() detects missing values in the entire DataFrame.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and the square of their length as values, only including words with length greater than 3.

Pandas

result = {word: len(word) [1] 2 for word in words if len(word) [2] 3}

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

A**

B>

C>=

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' instead of '**' for power.

Using '<' instead of '>' for filtering.

The first blank uses ** to square the length. The second blank uses > to filter words longer than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys, values from the original dict, and only include items with values greater than 0.

Pandas

result = [1]: [2] for k, v in data.items() if v [3] 0

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys.

Using '<' instead of '>' in the condition.

Keys are converted to uppercase with k.upper(), values are v, and only items with values greater than 0 are included.