Chapter Exam

Chapter-2 Data Handling using Pandas - II — Online MCQ Test

Q: What does NaN stand for in pandas?

Answer: A. Not a Number — NaN is a standard representation in pandas and NumPy for missing or undefined numerical values.

Q: Which method is used to check for missing values in a pandas DataFrame?

Answer: A. isnull() — The isnull() method returns a boolean DataFrame indicating which values are NaN or missing.

Q: If a DataFrame has missing values represented as NaN, which method fills them with the mean value?

Answer: B. df.fillna(df.mean()) — The fillna() method combined with mean() fills NaN values with the mean of the respective column.

Q: Consider a student marks DataFrame. If you need to identify and count all missing values across the entire DataFrame, which method would you use?

Answer: B. df.isnull().sum().sum() — df.isnull() returns boolean values, .sum() counts NaN per column, and another .sum() totals across all columns.

INFORMATICS PRACTICES · Grade 12 · CBSE(NCERT)

Practice Chapter-2 Data Handling using Pandas - II with a free chapter-wise online MCQ test for CBSE(NCERT) Grade 12 INFORMATICS PRACTICES. This chapter covers: DataFrame operations - missing values - NaN - descriptive statistics - mean - median - mode - sorting - CSV import - CSV export. AI-generated questions from basic to board-exam level, with instant results and explanations.

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Chapter-2 Data Handling using Pandas - II — Important Questions & Answers (FAQ)

Frequently asked questions from CBSE(NCERT) Grade 12 INFORMATICS PRACTICES — Chapter-2 Data Handling using Pandas - II, with answers and explanations. These are sample questions; the exam has its own separate question set.

What does NaN stand for in pandas?

A. Not a Number ✓
B. Not a Name
C. Null and Null
D. No Available Number

Answer: A. Not a Number
NaN is a standard representation in pandas and NumPy for missing or undefined numerical values.

Which method is used to check for missing values in a pandas DataFrame?

A. isnull() ✓
B. ismissing()
C. findnull()
D. checknull()

Answer: A. isnull()
The isnull() method returns a boolean DataFrame indicating which values are NaN or missing.

If a DataFrame has missing values represented as NaN, which method fills them with the mean value?

A. df.fill_na()
B. df.fillna(df.mean()) ✓
C. df.replace_missing()
D. df.impute_mean()

Answer: B. df.fillna(df.mean())
The fillna() method combined with mean() fills NaN values with the mean of the respective column.

Consider a student marks DataFrame. If you need to identify and count all missing values across the entire DataFrame, which method would you use?

A. df.count_na()
B. df.isnull().sum().sum() ✓
C. df.missing_values()
D. df.count_missing()

Answer: B. df.isnull().sum().sum()
df.isnull() returns boolean values, .sum() counts NaN per column, and another .sum() totals across all columns.

Consider a scenario where you need to keep track of which values were originally missing after imputation. What would be the best approach?

A. Don't impute, just use dropna()
B. Create a separate boolean column marking NaN locations before imputation ✓
C. Impute and then check describe() for changes
D. Use fillna() with a unique marker value instead of statistics

Answer: B. Create a separate boolean column marking NaN locations before imputation
Creating a mask column before imputation preserves information about original missing values for auditing and analysis.