Left and right joins in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ data_output

intermediate

Output of a left join on two DataFrames

Given two DataFrames df1 and df2, what is the output of the left join on column key?

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value1': [1, 2, 3]})
df2 = pd.DataFrame({'key': ['B', 'C', 'D'], 'value2': [4, 5, 6]})

result = pd.merge(df1, df2, how='left', on='key')
print(result)

A

  key  value1  value2
0   A       1     NaN
1   B       2     4.0
2   C       3     5.0

B

  key  value1  value2
0   B       2     4.0
1   C       3     5.0
2   D     NaN     6.0

C

  key  value1  value2
0   A       1     4.0
1   B       2     5.0
2   C       3     6.0

D

  key  value1  value2
0   A       1     NaN
1   B       2     NaN
2   C       3     NaN

💡 Hint

Remember, a left join keeps all rows from the left DataFrame and matches rows from the right DataFrame where keys match.

A left join keeps all rows from the left DataFrame (df1). For keys that don't exist in the right DataFrame (df2), the joined columns get NaN. Here, 'A' is only in df1, so value2 is NaN for that row.

❓ data_output

intermediate

Output of a right join on two DataFrames

What is the output of the right join on column key for the given DataFrames?

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({'key': ['X', 'Y', 'Z'], 'value1': [7, 8, 9]})
df2 = pd.DataFrame({'key': ['Y', 'Z', 'W'], 'value2': [10, 11, 12]})

result = pd.merge(df1, df2, how='right', on='key')
print(result)

A

  key  value1  value2
0   X     7.0      NaN
1   Y     8.0      10
2   Z     9.0      11

B

  key  value1  value2
0   X     7.0      10
1   Y     8.0      11
2   Z     9.0      12

C

  key  value1  value2
0   Y     NaN      10
1   Z     NaN      11
2   W     NaN      12

D

  key  value1  value2
0   Y     8.0      10
1   Z     9.0      11
2   W     NaN      12

💡 Hint

Right join keeps all rows from the right DataFrame and matches from the left.

Right join keeps all keys from df2. 'W' is only in df2, so value1 is NaN for that row. 'Y' and 'Z' exist in both, so values are matched.

🧠 Conceptual

advanced

Understanding join types in pandas

Which statement correctly describes the difference between a left join and a right join in pandas?

A Left join keeps only matching rows; right join keeps all rows from both DataFrames.

B Left join keeps all rows from the left DataFrame; right join keeps all rows from the right DataFrame.

C Left join keeps all rows from the right DataFrame; right join keeps all rows from the left DataFrame.

D Left join and right join both keep only matching rows from both DataFrames.

💡 Hint

Think about which DataFrame's rows are always kept in each join type.

Left join keeps all rows from the left DataFrame and matches rows from the right. Right join keeps all rows from the right DataFrame and matches rows from the left.

🔧 Debug

advanced

Identify the error in join code

What error will this code produce?

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B'], 'val1': [1, 2]})
df2 = pd.DataFrame({'key': ['B', 'C'], 'val2': [3, 4]})

result = pd.merge(df1, df2, how='left', on='keys')
print(result)

A KeyError: 'keys'

B TypeError: merge() got an unexpected keyword argument 'how'

C ValueError: columns overlap but no suffix specified

D No error; outputs merged DataFrame

💡 Hint

Check the column name used for joining.

The code tries to join on column 'keys' which does not exist in either DataFrame. This causes a KeyError.

🚀 Application

expert

Combine two DataFrames with left and right joins to find unmatched rows

You have two DataFrames df1 and df2. How can you find rows in df1 that do not have matching keys in df2 using joins?

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'val1': [1, 2, 3, 4]})
df2 = pd.DataFrame({'key': ['B', 'C'], 'val2': [5, 6]})

A Perform a full outer join and drop rows with NaN values.

B Perform a right join and filter rows where the left DataFrame columns are NaN.

C Perform a left join and filter rows where the right DataFrame columns are NaN.

D Perform an inner join and select rows with NaN values.

💡 Hint

Think about which join keeps all rows from df1 and how to identify unmatched rows.

A left join keeps all rows from df1. Rows with no match in df2 will have NaN in df2 columns. Filtering these rows finds unmatched keys.