Identifying missing values (isnull, isna) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of isnull() on a DataFrame

What is the output of the following code snippet?

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': [1, np.nan, 3],
    'B': [np.nan, 2, 3]
})
result = df.isnull()
print(result)

A

       A      B
0  False   True
1   True  False
2  False  False

B

       A      B
0  False   True
1   True   True
2  False  False

C

       A      B
0  False  False
1   True   True
2  False  False

D

       A      B
0   True  False
1  False   True
2  False  False

💡 Hint

Remember that isnull() returns True where values are missing (NaN).

The DataFrame has NaN at positions (0, B) and (1, A). isnull() marks these as True, others as False.

❓ data_output

intermediate

Counting missing values with isna()

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

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'X': [np.nan, 1, 2, np.nan],
    'Y': [3, 4, np.nan, 6]
})
missing_counts = df.isna().sum()
print(missing_counts)

A

X    2
Y    1
dtype: int64

B

X    1
Y    2
dtype: int64

C

X    0
Y    0
dtype: int64

D

X    2
Y    2
dtype: int64

💡 Hint

Count how many NaN values appear in each column.

Column X has NaN at positions 0 and 3 (2 total). Column Y has NaN at position 2 (1 total).

🔧 Debug

advanced

Identify the error in using isnull() on a list

What error will this code raise?

Data Analysis Python

import pandas as pd
values = [1, None, 3]
result = values.isnull()
print(result)

A No error, prints [False, True, False]

B TypeError: isnull() missing 1 required positional argument

C AttributeError: 'list' object has no attribute 'isnull'

D ValueError: Cannot convert list to DataFrame

💡 Hint

Check if the list type supports isnull() method.

The isnull() method is a pandas DataFrame or Series method, not available on Python lists.

🚀 Application

advanced

Filter rows with missing values using isna()

Which code snippet correctly filters rows with any missing values in DataFrame df?

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': [1, np.nan, 3],
    'B': [4, 5, np.nan]
})

A df[df.isna().all(axis=0)]

B df[df.isna().any(axis=1)]

C df[df.isna().any(axis=0)]

D df[df.isna().all(axis=1)]

💡 Hint

Use isna() with any() along rows to find rows with missing values.

any(axis=1) checks if any column in a row is missing, filtering those rows.

🧠 Conceptual

expert

Difference between isnull() and isna() in pandas

Which statement best describes the difference between pandas isnull() and isna() methods?

A isnull() detects missing values, isna() detects empty strings.

B isnull() detects only None values, while isna() detects only NaN values.

C isnull() works only on DataFrames, isna() works only on Series.

D They are exactly the same; both detect missing values identically.

💡 Hint

Check pandas documentation for isnull() and isna() methods.

In pandas, isnull() and isna() are aliases and behave identically to detect missing values.