Merging on index in Pandas - Step-by-Step Execution

Concept Flow - Merging on index

Start with two DataFrames

↓

Identify index columns

↓

Merge on index

↓

Create merged DataFrame

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Output

We start with two tables, identify their index columns, merge them using these indexes, and produce a combined table.

Execution Sample

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]}, index=['x', 'y'])
df2 = pd.DataFrame({'B': [3, 4]}, index=['x', 'y'])

merged = df1.merge(df2, left_index=True, right_index=True)

This code merges two DataFrames on their index labels, combining columns side by side.

Execution Table

Step	Action	df1 Index	df1 Columns	df2 Index	df2 Columns	Merge Condition	Merged Output
1	Create df1	['x', 'y']	['A']	N/A	N/A	N/A	{'x': {'A':1}, 'y': {'A':2}}
2	Create df2	N/A	N/A	['x', 'y']	['B']	N/A	{'x': {'B':3}, 'y': {'B':4}}
3	Merge on index	['x', 'y']	['A']	['x', 'y']	['B']	left_index=True, right_index=True	{'x': {'A':1, 'B':3}, 'y': {'A':2, 'B':4}}
4	Output merged DataFrame	['x', 'y']	['A', 'B']	['x', 'y']	['B']	Indexes matched	A B x 1 3 y 2 4

💡 All index labels matched, merge completed successfully.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
df1	undefined	{'x':1, 'y':2} with column A	{'x':1, 'y':2} with column A	{'x':1, 'y':2} with column A	{'x':1, 'y':2} with column A
df2	undefined	undefined	{'x':3, 'y':4} with column B	{'x':3, 'y':4} with column B	{'x':3, 'y':4} with column B
merged	undefined	undefined	undefined	{'x': {'A':1, 'B':3}, 'y': {'A':2, 'B':4}}	{'x': {'A':1, 'B':3}, 'y': {'A':2, 'B':4}}

Key Moments - 2 Insights

Why do we use left_index=True and right_index=True in merge?

Because we want to join the two DataFrames based on their index labels, not on columns. The execution_table row 3 shows the merge condition using these parameters.

What happens if the indexes do not match exactly?

Rows with non-matching indexes will be excluded by default in inner merge. This is implied in the exit_note where all indexes matched, so all rows appear in the output.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what is the merge condition used?

A left_index=True, right_index=True

B on='A'

C on='B'

D left_on='A', right_on='B'

💡 Hint

Check the 'Merge Condition' column at step 3 in execution_table.

At which step does the merged DataFrame first appear?

A Step 2

B Step 3

C Step 1

D Step 4

💡 Hint

Look at the 'Merged Output' column in execution_table to see when merged data is created.

If df2 had an index label 'z' not in df1, what would happen to that row in the merged DataFrame?

A It would appear with NaN values for df1 columns

B It would cause an error

C It would be excluded from the merged DataFrame

D It would replace df1 rows

💡 Hint

By default, merge on index is inner join, so only matching indexes appear (see exit_note).

Concept Snapshot

Merging on index in pandas:
Use df1.merge(df2, left_index=True, right_index=True)
This joins DataFrames by their index labels.
Only rows with matching indexes appear by default.
Result combines columns side by side.
Useful when index is meaningful key.

Full Transcript

This visual execution shows how to merge two pandas DataFrames using their index labels. First, two DataFrames df1 and df2 are created with indexes 'x' and 'y'. Then, the merge function is called with left_index=True and right_index=True to join them on their indexes. The merged DataFrame contains columns from both, aligned by index. The execution table traces each step, showing the indexes and columns involved, the merge condition, and the resulting merged output. Key moments clarify why the index parameters are used and what happens if indexes do not match. The quiz tests understanding of merge conditions, step when merge occurs, and behavior with unmatched indexes. The snapshot summarizes the syntax and behavior for quick reference.

Practice

1. What does setting left_index=True and right_index=True do when merging two pandas DataFrames?

easy

A. It merges the DataFrames by ignoring the indexes completely.

B. It merges the DataFrames using their column names as the key.

C. It merges the DataFrames by concatenating columns without any key.

D. It merges the DataFrames using their row indexes as the key.

Solution

1. Step 1: Understand the role of indexes in merge

   Indexes are row labels in pandas DataFrames. Using left_index=True and right_index=True tells pandas to join on these row labels instead of columns.

2. Step 2: Effect of these parameters in merge

   When both are True, pandas matches rows based on their index values, combining rows with the same index from both DataFrames.

3. Final Answer:

   It merges the DataFrames using their row indexes as the key. -> Option D

4. Quick Check:

   Merging on indexes = left_index=True and right_index=True [OK]

Hint: Indexes join rows by labels, not columns [OK]

Common Mistakes:

• Thinking merge always uses columns as keys
• Confusing index merge with concatenation
• Ignoring the need to set both left_index and right_index

2. Which of the following is the correct syntax to merge two DataFrames df1 and df2 on their indexes with an inner join?

easy

A. pd.merge(df1, df2, on='index', how='inner')

B. pd.merge(df1, df2, left_index=True, right_index=True, how='inner')

C. pd.merge(df1, df2, index=True, how='inner')

D. pd.merge(df1, df2, left_on='index', right_on='index', how='inner')

Solution

1. Step 1: Identify correct parameters for index merge

   To merge on indexes, pandas requires left_index=True and right_index=True. Using on='index' or left_on='index' is incorrect because 'index' is not a column name.

2. Step 2: Confirm the join type

   The how='inner' parameter specifies an inner join, which keeps only matching index rows.

3. Final Answer:

   pd.merge(df1, df2, left_index=True, right_index=True, how='inner') -> Option B

4. Quick Check:

   Use left_index and right_index for index merge [OK]

Hint: Use left_index and right_index, not 'on' for index merge [OK]

Common Mistakes:

• Using on='index' instead of left_index=True
• Confusing index merge with column merge
• Forgetting to set both left_index and right_index

3. Given the following DataFrames:

df1:
       A
idx
1      10
2      20
3      30


df2:
       B
idx
2     200
3     300
4     400

What is the result of pd.merge(df1, df2, left_index=True, right_index=True, how='inner')?

medium

A. {'idx': [2, 3], 'A': [20, 30], 'B': [200, 300]}

B. {'idx': [1, 2, 3], 'A': [10, 20, 30], 'B': [null, 200, 300]}

C. {'idx': [1, 2, 3, 4], 'A': [10, 20, 30, null], 'B': [null, 200, 300, 400]}

D. {'idx': [2, 3, 4], 'A': [20, 30, null], 'B': [200, 300, 400]}

Solution

1. Step 1: Understand inner join on indexes

   An inner join keeps only rows where the index exists in both DataFrames. Here, indexes 2 and 3 are common.

2. Step 2: Combine matching rows

   Rows with index 2 have A=20 and B=200, index 3 have A=30 and B=300. Rows with index 1 and 4 are excluded.

3. Final Answer:

   {'idx': [2, 3], 'A': [20, 30], 'B': [200, 300]} -> Option A

4. Quick Check:

   Inner join keeps only common indexes [OK]

Hint: Inner join keeps only matching index rows [OK]

Common Mistakes:

• Including rows with indexes not in both DataFrames
• Confusing inner join with outer join
• Expecting nulls in inner join result

4. Consider this code snippet:

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]}, index=[0, 1])
df2 = pd.DataFrame({'B': [3, 4]}, index=[1, 2])

result = pd.merge(df1, df2, left_index=True, right_index=True, how='outer')
print(result)

What is the error in this code?

medium

A. No error; the code runs and merges correctly.

B. Missing 'on' parameter causes a KeyError.

C. left_index and right_index cannot be used together.

D. DataFrames must have the same index values for merge.

Solution

1. Step 1: Check parameters used in merge

   The code uses left_index=True and right_index=True correctly to merge on indexes with how='outer'.

2. Step 2: Understand outer join behavior

   Outer join includes all indexes from both DataFrames, filling missing values with NaN. This is valid and runs without error.

3. Final Answer:

   No error; the code runs and merges correctly. -> Option A

4. Quick Check:

   left_index and right_index merge works fine [OK]

Hint: Outer join merges all indexes, no error with index merge [OK]

Common Mistakes:

• Assuming missing 'on' causes error when using index merge
• Thinking left_index and right_index cannot be combined
• Believing indexes must match exactly for merge

5. You have two DataFrames:

df1:
       Score
Student
Alice     85
Bob       90
Charlie   78


df2:
       Grade
Student
Bob       B
Charlie   C
David     A

You want to merge them so that all students from df1 appear, and matching grades from df2 are added. Missing grades should show as NaN. Which code achieves this?

hard

A. pd.merge(df1, df2, left_index=True, right_index=True, how='right')

B. pd.merge(df1, df2, left_index=True, right_index=True, how='inner')

C. pd.merge(df1, df2, left_index=True, right_index=True, how='left')

D. pd.merge(df1, df2, left_on='Student', right_on='Student', how='outer')

Solution

1. Step 1: Identify merge type for all df1 rows

   To keep all rows from df1 and add matching data from df2, use a left join.

2. Step 2: Merge on indexes

   Both DataFrames use 'Student' as index, so set left_index=True and right_index=True to merge on indexes.

3. Final Answer:

   pd.merge(df1, df2, left_index=True, right_index=True, how='left') -> Option C

4. Quick Check:

   Left join on indexes keeps all df1 rows [OK]

Hint: Left join keeps all left DataFrame rows [OK]

Common Mistakes:

• Using inner join which drops unmatched df1 rows
• Using right join which keeps df2 rows instead
• Merging on columns instead of indexes