COGS 108 ASSIGNMENT 2: PANDAS &
DATA VISUALIZATION | 2026 UPDATED
WITH COMPLETE SOLUTIONS.
149 Questions with Answers and Detailed Rationales
100 PERCENT GUARANTEED PASS
INSTANT DOWNLOAD ANSWERS INCLUDED
IMPORTANCE OF THIS DOCUMENT
This comprehensive examination preparation guide has been meticulously developed to help you succeed in the
COGS 108 ASSIGNMENT 2: PANDAS & DATA VISUALIZATION | 2026 UPDATED WITH COMPLETE
SOLUTIONS.. It contains 149 carefully selected questions that reflect the most current exam content and testing
strategies. Each question is accompanied by a correct answer and a detailed rationale that explains the
underlying pathophysiology, pharmacology, or clinical reasoning.
Self-Assessment – Test your knowledge and Exam Preparation – Familiarize yourself with the
identify areas requiring further question format and content
study areas
Concept Reinforcement – Deepen your Confidence Building – Develop test-taking
understanding through strategies and reduce
evidence-based exam anxiety
rationales
Time Management – Practice answering
questions under simulated
exam conditions
Review Summary 149 Questions
Foundations - Application - COGS 108 Assignment 2 Pandas & DATA Visualization 2026 Updated WITH
Complete Solutions Cognitive Science / DATA Science Pandas & DATA Visualization Undergraduate YEAR
2-3 Lower-division DATA Science CORE
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Introduction TO Pandas 1-25 Dataframe, Columns, Values, Pandas, Operation
Series AND Dataframe
Objects
DATA Loading AND 26-50 Dataframe, Column, Right, Expression, Index
Inspection READ CSV HEAD
INFO Describe
Indexing Selection AND 51-75 Dataframe, Column, Score, Expression, Values
Filtering LOC ILOC Boolean
Masks
DATA Cleaning Handling 76-100 Dataframe, Column, Right, Values, Visualization
Missing Values Duplicates
AND TYPE Conversion
Grouping AND Aggregation 101-125 Column, Dataframe, Values, Returns, CITY
Groupby AGG Transform
Merging Joining AND 126-149 Dataframe, Chart, Expression, Column, Choice
Concatenating Dataframes
TOTAL 149 All questions include answers and detailed rationales
,Section A - Introduction TO Pandas Series AND Dataframe
Objects
Q1.
Given df with a non-default integer index (e.g., 10, 20, 30), what does df.loc[10:20] return
compared to df.iloc[10:20]?
A. Both return the same rows because the B. loc returns rows with labels 10 through 20
labels equal their positions here. inclusive; iloc returns rows at positions 10
through 19.
C. loc returns rows at positions 10 through D. Both raise KeyError because the integer
20; iloc returns rows with labels 10 through index is not a RangeIndex.
19.
Correct: B - loc returns rows with labels 10 through 20 inclusive; iloc returns rows at
positions 10 through 19.
Rationale:loc slices by label and is inclusive on both endpoints, so labels 10 and 20 are both
included. iloc slices by zero-based position and is exclusive on the stop, returning positions
10 through 19. This distinction is a core pandas indexing concept tested in Assignment 2.
Why the other answers are wrong:
A. Labels and positions coincide only when the index is a default RangeIndex; here they
diverge.
C. loc uses labels (inclusive), not positions, and iloc uses positions (exclusive stop), not labels.
D. A non-default integer index is perfectly valid and does not raise KeyError.
Reference: pandas Documentation (2025). Indexing and selecting data: .loc vs .iloc.
Q2.
A DataFrame df has columns ['A','B','C']. After running df2 = df[['A','B']].copy() and then
df2['A'] = 0, what is the state of df?
A. Column 'A' in df is also set to 0 because B. df raises a SettingWithCopyWarning and
of view semantics. 'A' becomes 0.
C. df is unchanged because .copy() creates D. df loses columns 'B' and 'C' because of
an independent DataFrame. chained selection.
Correct: C - df is unchanged because .copy() creates an independent DataFrame.
Rationale:.copy() returns a deep copy, so modifications to df2 do not propagate to df. Without
.copy(), the chained selection could produce a view and trigger SettingWithCopyWarning.
This tests understanding of view vs copy semantics in pandas.
Why the other answers are wrong:
Page 3
, Section A - Introduction TO Pandas Series AND Dataframe Objects
A. Only views propagate changes; .copy() explicitly breaks the link to the parent.
B. No warning is raised because .copy() removes ambiguity about view/copy behavior.
D. Selecting a subset of columns never mutates the original DataFrame's schema.
Reference: McKinney, W. (2022). Python for Data Analysis, 3rd Ed., Ch. 5.
Q3.
Which pandas operation best converts a wide DataFrame with columns
['id','2023','2024','2025'] into a long/tidy format with columns ['id','year','value']?
A. df.pivot(index='id', columns='year', B. pd.melt(df, id_vars='id', var_name='year',
values='value') value_name='value')
C. df.groupby('id').agg(list) D. df.stack(level=0).unstack()
Correct: B - pd.melt(df, id_vars='id', var_name='year', value_name='value')
Rationale:pd.melt reshapes wide to long by unpivoting specified columns into variable/value
pairs, exactly producing id-year-value triples. pivot does the reverse (long to wide). This is a
canonical tidy-data transformation in Assignment 2.
Why the other answers are wrong:
A. pivot converts long to wide, the opposite of what is needed here.
C. groupby().agg(list) aggregates values into lists but does not create a tidy long format.
D. stack/unstack shuffles MultiIndex levels without producing the requested id-year-value
schema.
Reference: Wickham, H. (2014). Tidy Data. JSS 59(10); pandas reshaping docs.
Q4.
When merging two DataFrames on a key with duplicate values in both, what does the
default how='inner' merge produce?
A. Only rows with unique matches, dropping B. A cartesian product of matching keys (all
duplicates. pairwise combinations).
C. A left join that keeps all left rows. D. An error because duplicate keys are not
allowed in merges.
Correct: B - A cartesian product of matching keys (all pairwise combinations).
Rationale:pandas merges perform a relational join; when keys are duplicated on both sides,
each matching pair produces a row, yielding a cartesian product for those keys. This behavior
is essential for diagnosing inflated row counts after merges.
Why the other answers are wrong:
A. Duplicates are not dropped; they multiply matches.
C. how='inner' keeps only intersecting keys, not all left rows.
D. pandas permits duplicate keys and does not raise an error.
Page 4
DATA VISUALIZATION | 2026 UPDATED
WITH COMPLETE SOLUTIONS.
149 Questions with Answers and Detailed Rationales
100 PERCENT GUARANTEED PASS
INSTANT DOWNLOAD ANSWERS INCLUDED
IMPORTANCE OF THIS DOCUMENT
This comprehensive examination preparation guide has been meticulously developed to help you succeed in the
COGS 108 ASSIGNMENT 2: PANDAS & DATA VISUALIZATION | 2026 UPDATED WITH COMPLETE
SOLUTIONS.. It contains 149 carefully selected questions that reflect the most current exam content and testing
strategies. Each question is accompanied by a correct answer and a detailed rationale that explains the
underlying pathophysiology, pharmacology, or clinical reasoning.
Self-Assessment – Test your knowledge and Exam Preparation – Familiarize yourself with the
identify areas requiring further question format and content
study areas
Concept Reinforcement – Deepen your Confidence Building – Develop test-taking
understanding through strategies and reduce
evidence-based exam anxiety
rationales
Time Management – Practice answering
questions under simulated
exam conditions
Review Summary 149 Questions
Foundations - Application - COGS 108 Assignment 2 Pandas & DATA Visualization 2026 Updated WITH
Complete Solutions Cognitive Science / DATA Science Pandas & DATA Visualization Undergraduate YEAR
2-3 Lower-division DATA Science CORE
All answers with rationales
,Table of Contents
Content Area Questions Key Topics
Introduction TO Pandas 1-25 Dataframe, Columns, Values, Pandas, Operation
Series AND Dataframe
Objects
DATA Loading AND 26-50 Dataframe, Column, Right, Expression, Index
Inspection READ CSV HEAD
INFO Describe
Indexing Selection AND 51-75 Dataframe, Column, Score, Expression, Values
Filtering LOC ILOC Boolean
Masks
DATA Cleaning Handling 76-100 Dataframe, Column, Right, Values, Visualization
Missing Values Duplicates
AND TYPE Conversion
Grouping AND Aggregation 101-125 Column, Dataframe, Values, Returns, CITY
Groupby AGG Transform
Merging Joining AND 126-149 Dataframe, Chart, Expression, Column, Choice
Concatenating Dataframes
TOTAL 149 All questions include answers and detailed rationales
,Section A - Introduction TO Pandas Series AND Dataframe
Objects
Q1.
Given df with a non-default integer index (e.g., 10, 20, 30), what does df.loc[10:20] return
compared to df.iloc[10:20]?
A. Both return the same rows because the B. loc returns rows with labels 10 through 20
labels equal their positions here. inclusive; iloc returns rows at positions 10
through 19.
C. loc returns rows at positions 10 through D. Both raise KeyError because the integer
20; iloc returns rows with labels 10 through index is not a RangeIndex.
19.
Correct: B - loc returns rows with labels 10 through 20 inclusive; iloc returns rows at
positions 10 through 19.
Rationale:loc slices by label and is inclusive on both endpoints, so labels 10 and 20 are both
included. iloc slices by zero-based position and is exclusive on the stop, returning positions
10 through 19. This distinction is a core pandas indexing concept tested in Assignment 2.
Why the other answers are wrong:
A. Labels and positions coincide only when the index is a default RangeIndex; here they
diverge.
C. loc uses labels (inclusive), not positions, and iloc uses positions (exclusive stop), not labels.
D. A non-default integer index is perfectly valid and does not raise KeyError.
Reference: pandas Documentation (2025). Indexing and selecting data: .loc vs .iloc.
Q2.
A DataFrame df has columns ['A','B','C']. After running df2 = df[['A','B']].copy() and then
df2['A'] = 0, what is the state of df?
A. Column 'A' in df is also set to 0 because B. df raises a SettingWithCopyWarning and
of view semantics. 'A' becomes 0.
C. df is unchanged because .copy() creates D. df loses columns 'B' and 'C' because of
an independent DataFrame. chained selection.
Correct: C - df is unchanged because .copy() creates an independent DataFrame.
Rationale:.copy() returns a deep copy, so modifications to df2 do not propagate to df. Without
.copy(), the chained selection could produce a view and trigger SettingWithCopyWarning.
This tests understanding of view vs copy semantics in pandas.
Why the other answers are wrong:
Page 3
, Section A - Introduction TO Pandas Series AND Dataframe Objects
A. Only views propagate changes; .copy() explicitly breaks the link to the parent.
B. No warning is raised because .copy() removes ambiguity about view/copy behavior.
D. Selecting a subset of columns never mutates the original DataFrame's schema.
Reference: McKinney, W. (2022). Python for Data Analysis, 3rd Ed., Ch. 5.
Q3.
Which pandas operation best converts a wide DataFrame with columns
['id','2023','2024','2025'] into a long/tidy format with columns ['id','year','value']?
A. df.pivot(index='id', columns='year', B. pd.melt(df, id_vars='id', var_name='year',
values='value') value_name='value')
C. df.groupby('id').agg(list) D. df.stack(level=0).unstack()
Correct: B - pd.melt(df, id_vars='id', var_name='year', value_name='value')
Rationale:pd.melt reshapes wide to long by unpivoting specified columns into variable/value
pairs, exactly producing id-year-value triples. pivot does the reverse (long to wide). This is a
canonical tidy-data transformation in Assignment 2.
Why the other answers are wrong:
A. pivot converts long to wide, the opposite of what is needed here.
C. groupby().agg(list) aggregates values into lists but does not create a tidy long format.
D. stack/unstack shuffles MultiIndex levels without producing the requested id-year-value
schema.
Reference: Wickham, H. (2014). Tidy Data. JSS 59(10); pandas reshaping docs.
Q4.
When merging two DataFrames on a key with duplicate values in both, what does the
default how='inner' merge produce?
A. Only rows with unique matches, dropping B. A cartesian product of matching keys (all
duplicates. pairwise combinations).
C. A left join that keeps all left rows. D. An error because duplicate keys are not
allowed in merges.
Correct: B - A cartesian product of matching keys (all pairwise combinations).
Rationale:pandas merges perform a relational join; when keys are duplicated on both sides,
each matching pair produces a row, yielding a cartesian product for those keys. This behavior
is essential for diagnosing inflated row counts after merges.
Why the other answers are wrong:
A. Duplicates are not dropped; they multiply matches.
C. how='inner' keeps only intersecting keys, not all left rows.
D. pandas permits duplicate keys and does not raise an error.
Page 4
