Counting missing values in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to count missing values in the 'Age' column of the DataFrame df.

Pandas

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

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Aisna

Bsum

Ccount

Disnull

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'count' which counts non-missing values.

Using 'sum' directly without first identifying missing values.

The isna() method returns a boolean Series indicating missing values. To count them, you need to sum these booleans.

2fill in blank

medium

Complete the code to count the total number of missing values in the entire DataFrame df.

Pandas

total_missing = df.[1]().sum().sum()

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Anotnull

Bcount

Cisnull

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'count' which counts non-missing values.

Using 'dropna' which removes missing values instead of counting.

The isnull() method returns a DataFrame of booleans showing missing values. Summing twice counts all missing values.

3fill in blank

hard

Fix the error in the code to correctly count missing values in the 'Salary' column.

Pandas

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

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

Aisnull

Bnotnull

Ccount

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'count' which counts non-missing values.

Using 'notnull' which returns True for non-missing values.

To count missing values, use isnull() to get True for missing entries, then sum them.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 3 letters.

Pandas

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

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Alen(word)

Bword > 3

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' which compares a string to a number.

Using 'word' as value instead of length.

The dictionary comprehension uses len(word) for values and filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words longer than 4 letters.

Pandas

result = [1]: [2] for w in words if [3]

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Aw.upper()

Blen(w)

Clen(w) > 4

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of uppercase for keys.

Filtering with incorrect length condition.

The dictionary comprehension uses w.upper() as keys, len(w) as values, and filters words with length greater than 4.