Detecting missing values with isna() in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of isna() on a DataFrame column

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 2, 3]})
result = df['A'].isna()
print(result)

A

0     True
1    False
2     True
Name: A, dtype: bool

B

0    False
1     True
2    False
Name: A, dtype: bool

C

0    False
1    False
2    False
Name: A, dtype: bool

D

0     True
1     True
2     True
Name: A, dtype: bool

💡 Hint

Check which values in column 'A' are missing (None).

The isna() function returns True for missing values and False otherwise. In column 'A', only the second row has None, so only index 1 is True.

❓ data_output

intermediate

Count missing values in DataFrame

What is the output of this code that counts missing values in each column?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [1, 2, None, 4], 'Y': [None, None, 3, 4]})
missing_counts = df.isna().sum()
print(missing_counts)

A

X    0
Y    0
dtype: int64

B

X    2
Y    1
dtype: int64

C

X    1
Y    2
dtype: int64

D

X    1
Y    1
dtype: int64

💡 Hint

Count how many None values are in each column.

Column 'X' has one missing value (third row). Column 'Y' has two missing values (first and second rows). The sum() adds up True values from isna().

❓ visualization

advanced

Visualizing missing values with isna()

Which option correctly creates a heatmap showing missing values in the DataFrame?

Pandas

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 2, None], 'C': [1, 2, 3]})
sns.heatmap(____, cbar=False)
plt.show()

A df.isna().sum()

B df.notna()

C df

D df.isna()

💡 Hint

Heatmap needs a boolean DataFrame showing missing values.

The isna() method returns a DataFrame of True/False indicating missing values. Passing this to sns.heatmap() colors missing spots. Other options either show non-missing or numeric sums, which are not suitable.

🔧 Debug

advanced

Identify the error in missing value detection

What error will this code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
result = df.isna('A')
print(result)

A TypeError: isna() takes no arguments (1 given)

B KeyError: 'A'

C AttributeError: 'DataFrame' object has no attribute 'isna'

D No error, prints a DataFrame of False values

💡 Hint

Check the method signature of isna().

The isna() method does not accept any arguments. Passing 'A' causes a TypeError.

🚀 Application

expert

Filter rows with missing values in multiple columns

Given the DataFrame below, which code correctly filters rows where either column 'X' or 'Y' has missing values?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [1, None, 3, None], 'Y': [None, 2, None, 4]})

A df[df['X'].isna() | df['Y'].isna()]

B df[df.isna('X') | df.isna('Y')]

C df[df.isna()['X'] & df.isna()['Y']]

D df[df['X'].notna() | df['Y'].notna()]

💡 Hint

Use isna() on each column separately and combine with | for OR.

Option A correctly uses isna() on each column and combines with | (OR) to filter rows where either column is missing. Option A is invalid syntax. Option A uses AND instead of OR. Option A filters rows where either column is NOT missing.

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()