merge() for SQL-like joins in Pandas - Step-by-Step Execution

Concept Flow - merge() for SQL-like joins

Start with two DataFrames

↓

Choose join keys

↓

Select join type: inner, left, right, outer

↓

Match rows based on keys

↓

Combine matched rows

↓

Result: merged DataFrame

The merge() function takes two tables, matches rows by keys, and combines them based on the join type.

Execution Sample

Pandas

import pandas as pd

left = pd.DataFrame({'key': ['A', 'B', 'C'], 'left_val': [1, 2, 3]})
right = pd.DataFrame({'key': ['B', 'C', 'D'], 'right_val': [4, 5, 6]})

result = pd.merge(left, right, on='key', how='inner')
print(result)

This code merges two DataFrames on the 'key' column using an inner join, keeping only matching keys.

Execution Table

Step	Action	Left DataFrame Rows	Right DataFrame Rows	Matched Keys	Result Rows
1	Start with left DataFrame	[('A',1), ('B',2), ('C',3)]	N/A	N/A	N/A
2	Start with right DataFrame	N/A	[('B',4), ('C',5), ('D',6)]	N/A	N/A
3	Choose join keys 'key'	Keys: A,B,C	Keys: B,C,D	N/A	N/A
4	Select join type 'inner'	N/A	N/A	N/A	N/A
5	Match rows on keys	B, C	B, C	B, C	N/A
6	Combine matched rows	('B',2), ('C',3)	('B',4), ('C',5)	B, C	[('B',2,4), ('C',3,5)]
7	Output merged DataFrame	N/A	N/A	N/A	[('B',2,4), ('C',3,5)]

💡 All matching keys B and C combined; keys A and D excluded in inner join

Variable Tracker

Variable	Start	After Step 5	After Step 6	Final
left	[('A',1), ('B',2), ('C',3)]	[('A',1), ('B',2), ('C',3)]	[('A',1), ('B',2), ('C',3)]	[('A',1), ('B',2), ('C',3)]
right	[('B',4), ('C',5), ('D',6)]	[('B',4), ('C',5), ('D',6)]	[('B',4), ('C',5), ('D',6)]	[('B',4), ('C',5), ('D',6)]
matched_keys	None	['B', 'C']	['B', 'C']	['B', 'C']
result	None	None	[('B',2,4), ('C',3,5)]	[('B',2,4), ('C',3,5)]

Key Moments - 2 Insights

Why does the merged result only include keys 'B' and 'C' and not 'A' or 'D'?

Because the join type is 'inner', only keys present in both DataFrames are included. Rows with keys 'A' (only in left) and 'D' (only in right) are excluded, as shown in execution_table rows 5 and 6.

What happens if we change the join type to 'left'?

All keys from the left DataFrame are kept, including 'A'. Matching keys from right are merged, and unmatched right values become NaN. This would change the matched keys and result rows in execution_table.

Visual Quiz - 3 Questions

Look at the execution table at step 5, which keys are matched between left and right DataFrames?

A A and D

B B and C

C A, B, C

D C and D

💡 Hint

Check the 'Matched Keys' column at step 5 in the execution_table.

At which step does the merged DataFrame get its final rows?

A Step 4

B Step 5

C Step 7

D Step 6

💡 Hint

Look at the 'Result Rows' column in execution_table; final output is shown at step 7.

If the join type changes to 'outer', what happens to keys 'A' and 'D' in the result?

A Both 'A' and 'D' are included with NaN for missing values

B Only 'A' is included

C They are excluded

D Only 'D' is included

💡 Hint

Recall that 'outer' join keeps all keys from both DataFrames, filling missing matches with NaN.

Concept Snapshot

pandas.merge(left, right, on='key', how='type')
- Combines two DataFrames by matching keys
- 'how' controls join: inner, left, right, outer
- Inner join keeps only matching keys
- Left join keeps all left keys
- Outer join keeps all keys from both
- Result is a new DataFrame with combined columns

Full Transcript

The merge() function in pandas combines two tables by matching rows on specified keys. You start with two DataFrames, choose which columns to join on, and pick a join type like inner or left. The function finds matching keys and combines those rows. For example, an inner join keeps only keys present in both tables. The result is a new DataFrame with columns from both inputs. This process helps you combine related data easily, like matching customer info with orders.

Practice

1. What does the merge() function in pandas do?

easy

A. It filters rows based on a condition.

B. It sorts a DataFrame by column values.

C. It changes the data type of a column.

D. It combines two DataFrames based on matching columns.

Solution

1. Step 1: Understand the purpose of merge()

   The merge() function is designed to combine two tables (DataFrames) by matching values in specified columns.

2. Step 2: Compare with other options

   Sorting, filtering, and changing data types are different operations and not the main purpose of merge().

3. Final Answer:

   It combines two DataFrames based on matching columns. -> Option D

4. Quick Check:

   merge() joins tables on columns [OK]

Hint: merge() joins tables by matching columns [OK]

Common Mistakes:

• Confusing merge() with sort or filter functions
• Thinking merge() changes data types
• Assuming merge() works without matching columns

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

easy

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

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

C. pd.join(df1, df2, on='id', how='inner')

D. df1.merge(df2, on='id', how='outer')

Solution

1. Step 1: Recall correct merge syntax

   The correct pandas syntax for merging is pd.merge(left, right, on=column, how=type). Here, how='inner' specifies an inner join.

2. Step 2: Check each option

   pd.merge(df1, df2, on='id', how='inner') matches the correct syntax. df1.merge(df2, by='id', join='inner') uses wrong parameter names. pd.join(df1, df2, on='id', how='inner') uses pd.join which does not exist. df1.merge(df2, on='id', how='outer') uses how='outer' which is not inner join.

3. Final Answer:

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

4. Quick Check:

   Correct merge syntax = pd.merge(df1, df2, on='id', how='inner') [OK]

Hint: Use pd.merge(df1, df2, on='col', how='inner') [OK]

Common Mistakes:

• Using wrong parameter names like 'by' or 'join'
• Confusing merge() with join() method
• Mixing up 'inner' and 'outer' join types

3. Given the following code:

import pandas as pd

df1 = pd.DataFrame({'id': [1, 2, 3], 'value1': ['A', 'B', 'C']})
df2 = pd.DataFrame({'id': [2, 3, 4], 'value2': ['X', 'Y', 'Z']})
result = pd.merge(df1, df2, on='id', how='inner')
print(result)

What will be the output?

medium

A. id value1 value2
0 2 B X
1 3 C Y

B. id value1 value2
0 1 A NaN
1 2 B X
2 3 C Y

C. id value1 value2
0 2 B X
1 3 C Y
2 4 NaN Z

D. id value1
0 1 A
1 2 B
2 3 C

Solution

1. Step 1: Understand inner join behavior

   An inner join keeps only rows with matching 'id' values in both DataFrames. Here, common ids are 2 and 3.

2. Step 2: Check matching rows and columns

   Rows with id 2 and 3 appear in both df1 and df2, so their columns combine. Rows with id 1 or 4 are excluded.

3. Final Answer:

   id value1 value2 0 2 B X 1 3 C Y -> Option A

4. Quick Check:

   Inner join keeps only matching ids [OK]

Hint: Inner join keeps only rows with matching keys [OK]

Common Mistakes:

• Expecting all rows from both DataFrames
• Confusing inner join with left or outer join
• Ignoring NaN values in output

4. You run this code but get a KeyError:

import pandas as pd

df1 = pd.DataFrame({'key1': [1, 2], 'val': ['A', 'B']})
df2 = pd.DataFrame({'key2': [1, 2], 'val': ['X', 'Y']})
result = pd.merge(df1, df2, on='key1', how='inner')

What is the cause of the error?

medium

A. Column 'key1' does not exist in df2.

B. The merge function requires 'how' to be 'outer'.

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

D. You cannot merge on columns with different names.

Solution

1. Step 1: Check columns in both DataFrames

   df1 has column 'key1', but df2 has 'key2'. The merge tries to join on 'key1' which df2 lacks.

2. Step 2: Understand KeyError cause

   Because 'key1' is missing in df2, pandas raises a KeyError indicating the column is not found.

3. Final Answer:

   Column 'key1' does not exist in df2. -> Option A

4. Quick Check:

   Merge keys must exist in both DataFrames [OK]

Hint: Ensure join columns exist in both DataFrames [OK]

Common Mistakes:

• Assuming merge works with different column names without specifying
• Thinking 'how' parameter causes KeyError
• Believing row counts must match to merge

5. You have two DataFrames:

df_customers = pd.DataFrame({'customer_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie']})
df_orders = pd.DataFrame({'order_id': [101, 102, 103], 'customer_id': [2, 2, 4], 'amount': [250, 125, 300]})

You want to create a DataFrame showing all customers and their orders, including customers with no orders and orders with no matching customer. Which merge() call achieves this?

hard

A. pd.merge(df_customers, df_orders, on='customer_id', how='left')

B. pd.merge(df_customers, df_orders, on='customer_id', how='inner')

C. pd.merge(df_customers, df_orders, on='customer_id', how='outer')

D. pd.merge(df_customers, df_orders, on='customer_id', how='right')

Solution

1. Step 1: Understand join types

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

2. Step 2: Apply to problem context

   We want all customers (even without orders) and all orders (even without matching customers). Only outer join includes all rows from both sides.

3. Final Answer:

   pd.merge(df_customers, df_orders, on='customer_id', how='outer') -> Option C

4. Quick Check:

   Outer join includes all rows from both tables [OK]

Hint: Use how='outer' to keep all rows from both DataFrames [OK]

Common Mistakes:

• Using inner join excludes unmatched rows
• Left join excludes orders without customers
• Right join excludes customers without orders