merge() for SQL-style joins in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the merge() function do in pandas?

The merge() function combines two DataFrames based on common columns or indices, similar to SQL joins.

beginner

Name the four main types of joins you can perform with merge().

The four main join types are: inner, left, right, and outer joins.

beginner

What is an inner join in merge()?

An inner join returns only rows with keys that appear in both DataFrames.

intermediate

How do you specify the columns to join on in merge()?

Use the on parameter to specify the column name(s) to join on. You can also use left_on and right_on for different column names.

beginner

What happens if you perform a merge() without specifying the how parameter?

By default, merge() performs an inner join, returning only matching rows from both DataFrames.

Which how parameter value returns all rows from the left DataFrame and matching rows from the right?

A right

B left

C inner

D outer

The 'left' join returns all rows from the left DataFrame and matching rows from the right.

If you want to join on columns with different names in each DataFrame, which parameters do you use?

A on

B how

C index

D left_on and right_on

Use 'left_on' and 'right_on' to specify different column names to join on.

What type of join does merge() perform by default?

A inner

B left

C right

D outer

By default, 'merge()' performs an inner join.

Which join type returns all rows from both DataFrames, filling missing values with NaN?

A outer

B left

C right

D inner

An outer join returns all rows from both DataFrames, filling missing matches with NaN.

What parameter would you use to join DataFrames based on their index?

A on

B how

C left_index and right_index

D sort

Use 'left_index=True' and 'right_index=True' to join on indices.

Explain how you would use merge() to combine two DataFrames with a common column named 'id'.

Think about the simplest join on a shared column.

Describe the difference between an inner join and an outer join using merge().

Consider what happens to rows without matches.

Practice

1. What does the how parameter in merge() control when joining two DataFrames?

easy

A. The data types of the columns

B. The columns to join on

C. The order of rows in the result

D. The type of join: inner, left, right, or outer

Solution

1. Step 1: Understand the role of how in merge()

   The how parameter specifies the type of join to perform, such as inner, left, right, or outer join.

2. Step 2: Differentiate from other parameters

   Other parameters like on specify columns to join on, but how controls how rows are matched and combined.

3. Final Answer:

   The type of join: inner, left, right, or outer -> Option D

4. Quick Check:

   how = join type [OK]

Hint: Remember: 'how' means join type like SQL joins [OK]

Common Mistakes:

• Confusing 'how' with 'on' parameter
• Thinking 'how' controls column selection
• Assuming 'how' changes data types

2. Which of the following is the correct syntax to merge two DataFrames df1 and df2 on the column 'id' using an inner join?

easy

A. pd.merge(df1, df2, join='id', how='inner')

B. pd.merge(df1, df2, on='id', how='inner')

C. df1.merge(df2, on='id', type='inner')

D. pd.merge(df1, df2, on='id', join='inner')

Solution

1. Step 1: Identify correct parameter names

   The correct parameters for merging are on for the column and how for join type.

2. Step 2: Check syntax correctness

   pd.merge(df1, df2, on='id', how='inner') uses pd.merge(df1, df2, on='id', how='inner'), which is the correct syntax. Other options use incorrect parameter names like join or type.

3. Final Answer:

   pd.merge(df1, df2, on='id', how='inner') -> Option B

4. Quick Check:

   Correct parameters: on, how [OK]

Hint: Use 'on' for columns and 'how' for join type [OK]

Common Mistakes:

• Using 'join' instead of 'how'
• Using 'type' instead of 'how'
• Mixing parameter names

3. Given the following code, what is the output of result?

import pandas as pd

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

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

medium

A. key val1 val2
0 B 2 4
1 C 3 5

B. key val1 val2
0 A 1 4
1 B 2 5
2 C 3 6

C. key val1 val2
0 A 1 NaN
1 B 2 4
2 C 3 5

D. key val1 val2
0 B 2 NaN
1 C 3 NaN
2 D NaN 6

Solution

1. Step 1: Understand inner join on 'key'

   Inner join keeps only rows with keys present in both DataFrames: 'B' and 'C'.

2. Step 2: Match rows and combine columns

   For 'B': val1=2, val2=4; for 'C': val1=3, val2=5. Rows with 'A' and 'D' are excluded.

3. Final Answer:

   key val1 val2 0 B 2 4 1 C 3 5 -> Option A

4. Quick Check:

   Inner join = intersection keys [OK]

Hint: Inner join keeps only common keys [OK]

Common Mistakes:

• Including keys not in both DataFrames
• Confusing inner with outer join
• Misaligning values for keys

4. What is wrong with this code snippet?

import pandas as pd

df1 = pd.DataFrame({'id': [1, 2], 'val': ['A', 'B']})
df2 = pd.DataFrame({'key': [1, 3], 'val2': ['X', 'Y']})

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

medium

A. DataFrames must have the same number of rows to merge

B. The 'how' parameter cannot be 'left'

C. The merge function is missing the 'left_on' and 'right_on' parameters

D. Column 'id' does not exist in both DataFrames

Solution

1. Step 1: Check columns used for merging

   The code tries to merge on column 'id', but df2 has 'key' column instead of 'id'.

2. Step 2: Understand merge requirements

   For merging on different column names, left_on and right_on must be used, or columns must have the same name.

3. Final Answer:

   The merge function is missing the 'left_on' and 'right_on' parameters -> Option C

4. Quick Check:

   Merge columns must exist in both DataFrames or be specified [OK]

Hint: Check column names exist in both DataFrames or specify left_on/right_on [OK]

Common Mistakes:

• Assuming merge works with different column names without specifying
• Thinking 'how' parameter is invalid
• Believing row counts must match

5. You have two DataFrames: orders with columns order_id, customer_id, and amount, and customers with columns cust_id and name. How do you merge them to get all orders with customer names, even if some customers are missing in customers DataFrame?

hard

A. pd.merge(orders, customers, left_on='customer_id', right_on='cust_id', how='left')

B. pd.merge(orders, customers, on='customer_id', how='inner')

C. pd.merge(orders, customers, left_on='cust_id', right_on='customer_id', how='right')

D. pd.merge(orders, customers, on='cust_id', how='outer')

Solution

1. Step 1: Identify join keys with different column names

   Orders use 'customer_id', customers use 'cust_id', so use left_on and right_on to specify.

2. Step 2: Choose join type to keep all orders

   Use how='left' to keep all rows from orders, even if no matching customer exists.

3. Final Answer:

   pd.merge(orders, customers, left_on='customer_id', right_on='cust_id', how='left') -> Option A

4. Quick Check:

   Left join keeps all left rows [OK]

Hint: Use left_on/right_on for different keys and 'left' to keep all left rows [OK]

Common Mistakes:

• Using 'on' with different column names
• Using inner join and losing unmatched orders
• Swapping left_on and right_on