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C. Competency-based questions: HOTS Century #Experiential Learning
Skills
1. You downloaded a dataset from Kaggle, and the dates are written as “2025/08/15”, “15-08-2025”, and “Aug
15, 2025”. How would you handle this using Pandas?
2. You are given a column “Size” with entries like “small”, “S”, “s”, “Medium”, “M”, “L”, “Large”, and some blanks.
Explain how you would standardize this column.
Assertion and Reasoning Questions:
Direction: Questions 3-4, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions
by selecting the appropriate option given below:
a. Both A and R are true and R is the correct explanation of A.
b. Both A and R are true but R is not the correct explanation of A.
c. A is true but R is false.
d. A is false but R is true.
3. Assertion (A): The fillna() function is used to delete missing values from a dataset.
Reason (R): Missing values can be harmful during analysis.
4. Assertion (A): Using the str.strip() function helps improve text formatting during data cleaning.
Reason (R): It removes white spaces from the beginning and end of string values.
Statement-Based Questions:
Statement-Based Questions:

Direction: Questions 5-6, two statements are given: Statement 1 and Statement 2. Examine the statements
and mark the correct option:

a. Statement 1 is true, Statement 2 is true
b. Statement 1 is false, Statement 2 is false
c. Statement 1 is true, Statement 2 is false
d. Statement 1 is false, Statement 2 is true
5. Statement 1: pd.to_datetime() can be used to convert date columns into a common format.
Statement 2: Feature scaling is only useful for text columns.
6. Statement 1: In Pandas, .str.strip() helps remove extra spaces from string values.
Statement 2: fillna() function is used to drop duplicate rows.

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Century #Experiential Learning
Skills
Kaggle is a platform where data scientists and AI enthusiasts collaborate
to solve real-world problems. How do you think participating in Kaggle competitions can help you understand
the practical applications of AI in industries like finance or e-commerce?

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Deep Thinking Century #Interdisciplinary
Skills
Data cleaning is crucial for accurate analysis, and Pandas offers powerful tools to handle missing values,
duplicates, and inconsistencies. How do data cleaning techniques in Pandas, such as handling missing values
and duplicates, affect the accuracy of insights in data analysis? Can Pandas automate the process fully, or is
human judgment still required to ensure data integrity?

Theoretical and Practical Aspects of Data Processing 239