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

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

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.

Practice

1. What does the pandas.DataFrame.isna() function do?

easy

A. It converts all values to strings.

B. It fills missing values with zeros.

C. It removes rows with missing values.

D. It shows which values in the DataFrame are missing (NaN).

Solution

1. Step 1: Understand the purpose of isna()

   The isna() function checks each cell in a DataFrame and marks True if the value is missing (NaN), otherwise False.

2. Step 2: Compare options with function behavior

   Only It shows which values in the DataFrame are missing (NaN). correctly describes this behavior. Options B, C, and D describe other functions or actions.

3. Final Answer:

   It shows which values in the DataFrame are missing (NaN). -> Option D

4. Quick Check:

   isna() = marks missing values True [OK]

Hint: Remember: isna() marks missing spots True [OK]

Common Mistakes:

• Confusing isna() with fillna()
• Thinking isna() removes data
• Assuming isna() changes data types

2. Which of the following is the correct syntax to check missing values in a DataFrame named df?

easy

A. isna(df)

B. df.isna

C. df.isna()

D. df.isna[]

Solution

1. Step 1: Recall method call syntax in pandas

   Methods like isna() must be called with parentheses on the DataFrame object, so df.isna() is correct.

2. Step 2: Evaluate each option

   df.isna() uses correct syntax. df.isna misses parentheses, so it returns the method object, not the result. isna(df) is incorrect because isna is a method, not a standalone function. df.isna[] uses invalid bracket syntax.

3. Final Answer:

   df.isna() -> Option C

4. Quick Check:

   Method call needs parentheses () [OK]

Hint: Use parentheses to call isna(): df.isna() [OK]

Common Mistakes:

• Forgetting parentheses after isna
• Trying to call isna as a standalone function
• Using brackets instead of parentheses

3. Given the DataFrame:

import pandas as pd
data = {'A': [1, None, 3], 'B': [4, 5, None]}
df = pd.DataFrame(data)
print(df.isna())

What is the output?

medium

A. A B
0 False False
1 True False
2 False True

B. A B
0 True False
1 False True
2 False False

C. A B
0 False True
1 False False
2 True False

D. A B
0 False False
1 False True
2 True False

Solution

1. Step 1: Identify missing values in each column

   Column 'A' has None at index 1, so True there. Column 'B' has None at index 2, so True there. All other values are present (False).

2. Step 2: Match the True/False pattern to output

   A B 0 False False 1 True False 2 False True matches the pattern: index 1 in 'A' is True, index 2 in 'B' is True, others False.

3. Final Answer:

   A B 0 False False 1 True False 2 False True -> Option A

4. Quick Check:

   None values marked True by isna() [OK]

Hint: Look for None positions; isna() marks them True [OK]

Common Mistakes:

• Mixing row and column positions
• Confusing True and False values
• Assuming isna() replaces values

4. You run this code:

import pandas as pd
data = {'X': [10, 20, None], 'Y': [None, 40, 50]}
df = pd.DataFrame(data)
missing = df.isna

Why does missing.sum() cause an error?

medium

A. Because missing is a DataFrame, not a function.

B. Because isna is not called with parentheses when assigned.

C. Because isna() does not exist in pandas.

D. Because df is not defined.

Solution

1. Step 1: Understand method assignment without parentheses

   Assigning missing = df.isna stores the method itself, not the result. To get the result, you must call it with parentheses.

2. Step 2: Explain why missing.sum() causes error

   Assigning missing = df.isna stores the method object, not the DataFrame result. Method objects do not have a .sum() attribute--only DataFrames do. Thus, missing.sum() raises AttributeError.

3. Final Answer:

   Because isna is not called with parentheses when assigned. -> Option B

4. Quick Check:

   Assign method with () to get result [OK]

Hint: Assign with parentheses to get result, not method [OK]

Common Mistakes:

• Assigning method without parentheses and expecting result
• Confusing method object with method call
• Assuming isna() is not a pandas method

5. You have a DataFrame df with columns 'Age' and 'Salary'. You want to count how many missing values are in each column using isna(). Which code correctly does this?

hard

A. df.isna().sum()

B. df.isna.sum()

C. df.sum().isna()

D. df.isna().count()

Solution

1. Step 1: Use isna() to mark missing values

   The isna() method returns True for missing values and False otherwise.

2. Step 2: Sum True values per column to count missing

   Since True is treated as 1 and False as 0, summing over rows counts missing values per column. So df.isna().sum() gives the count per column.

3. Final Answer:

   df.isna().sum() -> Option A

4. Quick Check:

   Sum True from isna() to count missing [OK]

Hint: Sum isna() results to count missing per column [OK]

Common Mistakes:

• Forgetting parentheses after isna
• Using count() which counts all rows, not missing
• Applying sum() before isna()