Merge CSV Files with Different Headers

Merging two identical CSVs is easy. But what if they are slightly different?

File A: Name, Email, Phone File B: Name, Email, City

If you just paste them together, the "City" data will end up in the "Phone" column (or get lost). Disaster.

You need a Smart Merge (also known as a Union).

The Goal

You want a result that includes all unique columns:

Result: Name, Email, Phone, City

• File A rows will have City as empty.
• File B rows will have Phone as empty.

Method 1: Python (Pandas)

Pandas handles this automatically. It aligns columns by name.

import pandas as pd

df1 = pd.read_csv('file1.csv')
df2 = pd.read_csv('file2.csv')

# concat aligns columns automatically and fills missing with NaN
merged = pd.concat([df1, df2], ignore_index=True)

merged.to_csv('merged.csv', index=False)

This is the gold standard method.

Method 2: Excel Manual Alignment

If you have to use Excel:

1. Open File A.
2. Open File B.
3. Look at the headers. File B has "City".
4. Add a "City" column to File A (leave it blank).
5. Add a "Phone" column to File B (leave it blank).
6. Reorder the columns in File B so they match File A exactly.
7. Now Copy-Paste File B rows into File A.

Pros: No code. Cons: Extremely tedious and error-prone if you have many columns.

Method 3: Smart Merge Tools

Tools designed for this will map columns for you.

-> Merge CSV Tool

Look for features like "Auto-align columns" or "Union Merge".

Handling "Mismatched" Names

What if: File A: Email Address File B: Email

Computers see these as different columns. You will end up with two columns in your output.

Solution: Rename headers before merging. Standardize everything to Email.

Summary

• Don't just copy-paste mismatched files.
• Union approach preserves all data.
• Pandas concat is the best automated way.
• Renaming headers to match is crucial preparation.

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Merging messy files? HappyCSV automatically aligns columns and handles missing fields so you don't lose data.