Data validation checks in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Check for missing values in a DataFrame

What is the output of this code that checks for missing values in the DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, None, 4],
    'B': ['x', None, 'y', 'z']
})

result = df.isnull().sum()

A

A    1
B    1
dtype: int64

B

A    0
B    0
dtype: int64

C

A    1
B    0
dtype: int64

D

A    0
B    1
dtype: int64

💡 Hint

Use the isnull() method to find missing values and sum() to count them per column.

The DataFrame has one missing value in column 'A' and one in column 'B'. The isnull() method marks missing values as True, and sum() counts them.

❓ data_output

intermediate

Count unique values per column

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

Pandas

import pandas as pd

df = pd.DataFrame({
    'Color': ['red', 'blue', 'red', 'green'],
    'Shape': ['circle', 'square', 'circle', 'triangle']
})

unique_counts = df.nunique()

A

Color    4
Shape    4
dtype: int64

B

Color    3
Shape    3
dtype: int64

C

Color    3
Shape    4
dtype: int64

D

Color    4
Shape    3
dtype: int64

💡 Hint

The nunique() method counts distinct values per column.

The 'Color' column has three unique values: red, blue, green. The 'Shape' column has three unique values: circle, square, triangle.

🔧 Debug

advanced

Identify the error in data type validation

What error does this code raise when checking if all values in column 'Age' are integers?

Pandas

import pandas as pd

df = pd.DataFrame({'Age': [25, 30, 'thirty-five', 40]})

all_int = df['Age'].apply(lambda x: isinstance(x, int)).all()

A False

B True

C TypeError

D ValueError

💡 Hint

Check the data types of each value in the 'Age' column.

The third value is a string 'thirty-five', so the isinstance check returns False for that value, making all() return False.

🚀 Application

advanced

Detect duplicate rows in a DataFrame

Which option correctly returns a DataFrame containing only the duplicate rows?

Pandas

import pandas as pd

df = pd.DataFrame({
    'ID': [1, 2, 2, 3, 4, 4, 4],
    'Value': ['a', 'b', 'b', 'c', 'd', 'd', 'd']
})

A df[df.duplicated(keep='first')]

B df[df.duplicated()]

C df[df.duplicated(keep='last')]

D df[df.duplicated(keep=False)]

💡 Hint

Use duplicated() with keep=False to mark all duplicates as True.

duplicated(keep=False) marks all occurrences of duplicates as True, so filtering returns all duplicate rows.

🧠 Conceptual

expert

Understanding data validation with custom rules

You want to validate a DataFrame column 'Score' to ensure all values are between 0 and 100 inclusive. Which code snippet correctly returns True if all values meet this condition?

A (df['Score'] >= 0) & (df['Score'] <= 100).all()

B df['Score'].apply(lambda x: 0 <= x <= 100).all()

C df['Score'].between(0, 100).all()

D ((df['Score'] >= 0) & (df['Score'] <= 100)).all()

💡 Hint

Use the pandas between() method for inclusive range checks.

The between() method checks if values are between two bounds inclusive. Using .all() confirms all values meet the condition.