Why pandas merge many to many creates duplicate rows (and how to fix it)

Duplicate rows in pandas merge usually appear in real-world datasets coming from SQL exports, logs, or APIs, where many-to-many relationships exist. This leads pandas to generate more rows than expected, often silently breaking downstream logic.

Quick Answer

Pandas merge many to many creates duplicate rows when the DataFrames have many-to-many relationships, creating a Cartesian product. Fix by aggregating or deduplicating before merging.

TL;DR

  • Merge many to many multiplies rows when DataFrames have many-to-many relationships
  • This is expected behavior, not a pandas bug
  • Always validate merge cardinality explicitly
  • Aggregate or deduplicate before merging

Problem Example

import pandas as pd

df1 = pd.DataFrame({'id': [1,1,2], 'val': [10,20,30]})
df2 = pd.DataFrame({'id': [1,1,2], 'amt': [40,50,60]})
print(f"df1: {len(df1)} rows, df2: {len(df2)} rows")
merged = pd.merge(df1, df2, on='id', how='inner')
print(f"merged: {len(merged)} rows")
print(merged)
# Output: 5 rows instead of expected 3

Root Cause Analysis

The DataFrames contain many-to-many relationships. Pandas performs a cross join for each matching key, creating a Cartesian product. This behavior is identical to SQL INNER JOIN semantics and often surprises developers transitioning from row-based assumptions to relational logic. Related factors:

  • Many-to-many relationships between DataFrames
  • No aggregation or deduplication before merge
  • No validation on key uniqueness

How to Detect This Issue

# Check for many-to-many relationships
many_to_many_count = df1.groupby('id').size().max() * df2.groupby('id').size().max()
print(f'Many-to-many relationships: {many_to_many_count}')

# Show many-to-many keys
many_to_many_keys = df1['id'].duplicated().any() and df2['id'].duplicated().any()
if many_to_many_keys:
    print('Many-to-many relationships detected')

Solutions

Solution 1: Aggregate before merge

df1_agg = df1.groupby('id')['val'].sum().reset_index()
df2_agg = df2.groupby('id')['amt'].sum().reset_index()
merged = pd.merge(df1_agg, df2_agg, on='id', how='inner')

Solution 2: Deduplicate before merge

df1_dedup = df1.drop_duplicates(subset='id', keep='first')
df2_dedup = df2.drop_duplicates(subset='id', keep='first')
merged = pd.merge(df1_dedup, df2_dedup, on='id', how='inner')

Solution 3: Validate during merge

merged = pd.merge(df1, df2, on='id', how='inner', validate='many_to_many')
# No validation error, as many-to-many is expected

Why validate Parameter Fails

Using validate='one_to_one' will raise a MergeError when many-to-many relationships exist. This is not a bug — it is pandas protecting you from a join that would silently multiply rows. If the relationship is expected to be many-to-many, use validate='many_to_many'. For one-to-many use validate='one_to_many'. For many-to-one use validate='many_to_one'.

Production-Safe Pattern

merged = pd.merge(df1, df2, on='id', how='inner', validate='many_to_many')
assert len(merged) == len(df1) * len(df2), 'Merge created unexpected rows'

Wrong Fixes That Make Things Worse

❌ Dropping duplicates after the merge: This hides the symptom but corrupts your data

❌ Using outer join ’to be safe’: This introduces NaN rows and breaks assumptions

❌ Ignoring row count changes: Always assert expected row counts after merge

Common Mistakes to Avoid

  • Not checking for many-to-many relationships before merge
  • Using inner join without understanding cardinality
  • Ignoring the validate parameter

Frequently Asked Questions

Q: Why does pandas merge many to many create more rows than expected?

When the DataFrames have many-to-many relationships, pandas creates a row for each combination (Cartesian product).

Q: Is this a pandas bug?

No. This behavior follows SQL join semantics. Pandas is correctly preserving relational cardinality.

Q: How do I prevent duplicate rows in pandas merge many to many?

Aggregate or deduplicate DataFrames using groupby() or drop_duplicates() before merging, or use the validate parameter to catch the issue early.

→ Fix pandas merge on multiple columns gives wrong result → Why pandas merge duplicates rows after groupby → Fix pandas left join returns unexpected rows → Fix pandas merge raises MergeError

Next Steps

After fixing this issue:

  1. Validate your merge with the validate parameter
  2. Add unit tests to catch similar issues
  3. Review related merge problems above