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.

Practice

1. What does a left join do when combining two tables in data analysis?

easy

A. Keeps all rows from both tables, matching or not.

B. Keeps all rows from the left table and adds matching rows from the right table.

C. Keeps only rows that match in both tables.

D. Keeps all rows from the right table and adds matching rows from the left table.

Solution

1. Step 1: Understand the role of the left table in a left join

   A left join keeps every row from the left table regardless of matches in the right table.

2. Step 2: Understand how matching rows from the right table are added

   Only rows from the right table that match the left table's keys are added; unmatched right rows are ignored.

3. Final Answer:

   Keeps all rows from the left table and adds matching rows from the right table. -> Option B

4. Quick Check:

   Left join = all left rows + matching right rows [OK]

Hint: Left join keeps all left rows, adds right matches [OK]

Common Mistakes:

• Confusing left join with right join
• Thinking left join keeps unmatched right rows
• Believing left join only keeps matching rows

2. Which of the following is the correct syntax for a left join using pandas in Python?

easy

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

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

C. pd.join(df1, df2, how='left')

D. df1.left_join(df2, on='key')

Solution

1. Step 1: Recall pandas merge syntax for joins

   In pandas, the function to join tables is pd.merge(), specifying the join type with the 'how' parameter.

2. Step 2: Identify the correct parameters for a left join

   Using how='left' and on='key' correctly performs a left join on the specified key column.

3. Final Answer:

   pd.merge(df1, df2, how='left', on='key') -> Option A

4. Quick Check:

   pd.merge + how='left' = left join [OK]

Hint: Use pd.merge with how='left' for left join [OK]

Common Mistakes:

• Using pd.join instead of pd.merge
• Wrong 'how' parameter like 'right' or missing
• Calling non-existent left_join method

3. Given these two DataFrames:

df1 = pd.DataFrame({'id': [1, 2, 3], 'value': ['A', 'B', 'C']})
df2 = pd.DataFrame({'id': [2, 3, 4], 'score': [90, 80, 70]})

What is the result of pd.merge(df1, df2, how='left', on='id')?

medium

A. [{'id':1,'value':'A','score':NaN},{'id':2,'value':'B','score':90},{'id':3,'value':'C','score':80}]

B. [{'id':2,'value':'B','score':90},{'id':3,'value':'C','score':80},{'id':4,'value':NaN,'score':70}]

C. [{'id':1,'value':'A','score':NaN},{'id':2,'value':'B','score':90},{'id':3,'value':'C','score':80},{'id':4,'value':NaN,'score':70}]

D. [{'id':1,'value':'A','score':NaN},{'id':2,'value':'B','score':NaN},{'id':3,'value':'C','score':NaN}]

Solution

1. Step 1: Identify the left and right tables

   df1 is the left table with ids 1, 2, 3; df2 is the right table with ids 2, 3, 4.

2. Step 2: Perform left join on 'id'

   All rows from df1 appear. For id=1, no match in df2, so score is NaN. For ids 2 and 3, scores 90 and 80 are added. id=4 from df2 is ignored.

3. Final Answer:

   [{'id':1,'value':'A','score':NaN},{'id':2,'value':'B','score':90},{'id':3,'value':'C','score':80}] -> Option A

4. Quick Check:

   Left join keeps all df1 rows + matching df2 rows [OK]

Hint: Left join keeps all left rows, unmatched right rows ignored [OK]

Common Mistakes:

• Including unmatched right rows in output
• Replacing unmatched left values with NaN incorrectly
• Confusing left and right join results

4. You run this code but get an error:

pd.merge(df1, df2, how='right', key='id')

What is the error and how to fix it?

medium

A. Error: DataFrames must have same columns. Fix by renaming columns.

B. Error: 'how' must be 'left' or 'inner'. Fix by changing 'right' to 'left'.

C. Error: 'key' is not a valid argument. Fix by using 'on='id'' instead.

D. No error, code runs fine.

Solution

1. Step 1: Identify the incorrect parameter name

   The parameter to specify join keys in pd.merge is 'on', not 'key'.

2. Step 2: Correct the parameter name

   Replace 'key='id'' with 'on='id'' to fix the error.

3. Final Answer:

   Error: 'key' is not a valid argument. Fix by using 'on='id'' instead. -> Option C

4. Quick Check:

   Use 'on' for join keys in pd.merge [OK]

Hint: Use 'on' not 'key' for join columns in pd.merge [OK]

Common Mistakes:

• Using 'key' instead of 'on' parameter
• Changing 'how' incorrectly
• Assuming no error occurs

5. You have two DataFrames:

df_customers = pd.DataFrame({'customer_id': [1, 2, 3], 'name': ['Ann', 'Bob', 'Cara']})
df_orders = pd.DataFrame({'order_id': [101, 102], 'customer_id': [2, 4], 'amount': [250, 300]})

You want to list all orders with customer names, including orders without matching customers. Which join should you use and why?

hard

A. Full outer join to keep all customers and all orders.

B. Left join on 'customer_id' to keep all customers and add matching orders.

C. Inner join on 'customer_id' to keep only matching customers and orders.

D. Right join on 'customer_id' to keep all orders and add matching customer names.

Solution

1. Step 1: Identify which table to keep all rows from

   You want all orders listed, even if no matching customer exists, so keep all rows from df_orders.

2. Step 2: Choose join type to keep all right table rows

   df_orders is the right table in the join. Using a right join keeps all rows from df_orders and adds matching customer names from df_customers.

3. Final Answer:

   Right join on 'customer_id' to keep all orders and add matching customer names. -> Option D

4. Quick Check:

   Right join keeps all right table rows [OK]

Hint: Use right join to keep all orders, add matching customers [OK]

Common Mistakes:

• Using left join keeps all customers, not all orders
• Using inner join drops unmatched orders
• Using full join adds unmatched customers unnecessarily