🔗 The One Thing Everyone Ignores in Data Merging

You’ve merged two tables. No errors.
But your row count just doubled. Or shrank. Or got weird.
No warnings. Just silent chaos.

It’s not your syntax.
It’s your keys.

😖 Why This Hurts

Unexpected duplicates and mismatches during joins are silent bugs.
Your analysis runs. Your charts render. But your conclusions? Broken.

🔍 The Fix: Audit Before You Merge

Most merge bugs aren’t code problems.
They’re data integrity problems.
Here’s how to catch them early.

✅ 3 Checks to Run Before Merging

1. Check for Duplicates in Join Keys

df['user_id'].duplicated().sum()

If you're using user_id as a key and it’s duplicated, you're not doing a 1:1 join.
That’s how accidental row multiplication happens.

Tip: Always confirm expected uniqueness in both tables.

2. Use Value Counts to Spot Mismatches

df['country'].value_counts(dropna=False)

Look for:

• Unexpected nulls

• Misspelled categories

• Rare values that shouldn’t exist

These silently break joins or exclude rows.

3. Do a Pre-Merge Anti-Join Check

merged = df1.merge(df2, on='id', how='left', indicator=True)
merged['_merge'].value_counts()

• left_only → rows in df1 with no match in df2

• right_only → rows in df2 with no match in df1

Use this to catch missing matches before they bite.

💡 Bonus: Always Compare Row Counts

After every merge:

print(len(df1), len(df2), len(merged))

If the math doesn't add up, stop and investigate.
The earlier you catch it, the less you break downstream.

📊 Poll

How often do you run anti-join checks before merging?
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