Identifying missing values (isnull, isna) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

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

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

Amean

Bsum

Cisnull

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of isnull() to identify missing values.

Using count() which counts non-missing values.

Using mean() which calculates average, not missing values.

The isnull() method returns a boolean Series indicating which values are missing (NaN).

2fill in blank

medium

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

Data Analysis Python

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

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

Aisna

Bcount

Cmean

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which counts non-missing values.

Using mean() which calculates average, not missing values.

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

The isna() method returns True for missing values. Summing these True values counts the missing entries.

3fill in blank

hard

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

Data Analysis Python

missing_data = df.[1]()

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

Aisnull

Bisna

Cmissing

Dnull

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'missing' or 'null' which are not pandas methods.

Confusing isna() and isnull() - both work but isnull() is more common.

The correct method to identify missing values is isnull(). The options 'missing' and 'null' are not valid pandas methods.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths only if length is greater than 3.

Data Analysis Python

lengths = {word: [1] for word in words if [2]

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' which compares string to number and causes error.

Using 'word' as value instead of length.

Not filtering words by length.

The dictionary comprehension uses len(word) as the value and filters words where length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values only if value is greater than 0.

Data Analysis Python

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

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

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

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

Using k or v incorrectly in keys or values.

The dictionary comprehension uses uppercase keys with k.upper(), values as v, and filters where value is greater than 0.