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.