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Merging & Concatenating

Here’s a quiz with 10 questions (including code-based ones) on merging and concatenating in Pandas. Each question includes options, the correct answer, and a justification.

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Question 1

What function is used to concatenate DataFrames in Pandas?

pd.merge()
pd.concat()
pd.append()
pd.join()

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Question 2

What is the default axis for concatenation in pd.concat()?

axis=0 (row-wise)
axis=1 (column-wise)
It depends on the input DataFrames
No default axis is set

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Question 3

Which of the following parameters ensures duplicate indices are handled when using pd.concat()?

keys
ignore_index
join
sort

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Question 4

What will be the result of the following code?

Python

import pandas as pd
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})
result = pd.concat([df1, df2], axis=1)
print(result)

A B A B
0 1 3 5 7
1 2 4 6 8
A B
0 1 3
1 2 4
2 5 7
3 6 8
A B A B
0 1 2 5 6
1 3 4 7 8
An error

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Question 5

What does the keys parameter do in pd.concat()?

Merges columns with the same names
Assign hierarchical index levels to concatenated DataFrames
Appends new keys as columns
Sorts the DataFrames before concatenation

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Question 6

What will be the result of the following code?

Python

import panads as pd
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})

result = pd.concat([df1, df2], keys=['df1', 'df2'])
print(result)

A B
0 1 3
1 2 4
2 5 7
3 6 8
A B
df1 0 1 3
1 2 4
df2 0 5 7
1 6 8
A B
0 1 3
1 2 4
0 5 7
1 6 8
A B
df1 0 1 3
1 2 4
df2 0 5 7
1 6 8

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Question 7

What function is used for a SQL-like merge in Pandas?

pd.concat()
pd.join()
pd.append()
pd.merge()

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Question 8

What will be the result of the following code?

Python

import pandas as pd
df1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'ID': [2, 3], 'Score': [90, 80]})

result = pd.merge(df1, df2, on='ID', how='inner')
print(result)

ID Name Score
0 1 Alice NaN
1 2 Bob 90.0
ID Name Score
0 2 Bob 90.0
ID Name Score
0 2 Bob 90
1 3 NaN 80
An error

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Question 9

Which parameter in pd.merge() specifies the type of join to perform?

on
how
sort
surfixes

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Question 10

What will be the result of the following code?

Python

import pandas as pd 
df1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'ID': [2, 3], 'Score': [90, 80]})

result = pd.merge(df1, df2, on='ID', how='outer', suffixes=('_df1', '_df2'))
print(result)

ID Name Score
0 1 Alice NaN
1 2 Bob 90.0
2 3 NaN 80.0
ID Name_df1 Score
0 1 Alice NaN
1 2 Bob 90.0
2 3 NaN 80.0
ID Name_df1 Score_df2
0 1 Alice NaN
1 2 Bob 90.0
2 3 NaN 80.0
An error

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