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Analyzing DataFrames

This quiz is designed to test your knowledge of analyzing DataFrames in Python using the Pandas library.

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Question 1

What will df.describe() return?

Python

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [24, 27, 22, 32],
        'Score': [85, 90, 88, 78]}
df = pd.DataFrame(data)

Summary statistics of all columns, including non-numeric ones
Summary statistics of numeric columns only
Error, because df.describe() requires numeric-only columns
None of the above

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Question 2

What is the output of df['Age'].mean()?

24
25.25
27.0
32

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Question 3

Which of the following methods is used to check for missing values in a DataFrame?

df.has_nan()
df.isnull()
df.fillna()
df.check_nan()

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Question 4

Given df, which command will filter rows where the Score is greater than 85?

df.filter(df['Score'] > 85)
df[df.Score > 85]
df.loc[df['Score'] > 85]
Both b and c

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Question 5

What does the following code do?

Python

df['Category'] = ['A', 'B', 'A', 'B']
df.groupby('Category')['Score'].mean()

Computes the mean Score grouped by the Category column
Adds a new column with grouped means
Creates a DataFrame with grouped means but does not modify df
Both a and c

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Question 6

How can you reset the index of a DataFrame?

df.reset_index()
df.set_index(None)
df.index_reset()
df.reindex()

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Question 7

What will the following code output?

Python

df[df['Name'] == 'Alice']

A DataFrame with all rows where Name is 'Alice'
A Series with the row where Name is 'Alice'
An error, because Name is not a numeric column
None of the above

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Question 8

What does df['Score'] > 80 return?

A filtered DataFrame
A Boolean Series
A DataFrame with True or False values
None of the above

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Question 9

How do you sort df by the Age column in descending order?

df.sort_values(by='Age', ascending=False)
df.sort(by='Age', desc=True)
df.sort(by='Age', ascending=False)
None of the above

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Question 10

What does df.info() provide?

Descriptive statistics for all columns
Data types, non-null counts, and memory usage of columns
DataFrame metadata, including shape and size
None of the above

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Pandas

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