Compare Two CSV Files (Find Differences)

You have data_v1.csv and data_v2.csv. You need to know:

• What rows were added?
• What rows were deleted?
• Which specific cells changed?

Staring at them side-by-side is impossible. Here is how to automate the comparison.

Method 1: Online Diff Tool (Easiest)

For quick visual comparison.

1. Upload File A (Old).
2. Upload File B (New).
3. The tool highlights changes: Green (Added), Red (Deleted), Yellow (Changed).

-> Compare CSV Files

Method 2: Excel "Inquire" or Conditional Formatting

Quick Visual Check:

1. Open both files.
2. Copy data from File B into a new sheet in File A.
3. Select all data in both sheets.
4. Home > Conditional Formatting > Highlight Cells Rules > Duplicate Values.
5. Switch to "Unique" values.
6. Unique values (changes) will be highlighted.

Formula Method (True/False): If Sheet1 and Sheet2 are sorted identically: In Sheet3 cell A1: =IF(Sheet1!A1=Sheet2!A1, "Match", "CHANGE") Drag this formula across all rows/cols.

Pros: Built-in. Cons: Fails if rows are inserted/deleted (misaligns everything).

Method 3: Command Line (diff)

If you are on Mac or Linux, the diff command is built-in.

diff file1.csv file2.csv

Output:

< 1,John,Manager
---
> 1,John,Senior Manager

< means line from file 1. > means line from file 2.

Better Command: vimdiff

vimdiff file1.csv file2.csv

Opens a side-by-side view in the terminal.

Pros: Fast, free. Cons: Hard to read for non-developers; sensitive to row sorting.

Method 4: Python (Pandas)

For detailed analysis (e.g., "Give me a list of all new IDs").

import pandas as pd

df1 = pd.read_csv('old.csv')
df2 = pd.read_csv('new.csv')

# Find rows in df2 that are not in df1 (Added)
new_rows = df2[~df2['id'].isin(df1['id'])]

# Find rows in df1 that are not in df2 (Deleted)
deleted_rows = df1[~df1['id'].isin(df2['id'])]

print("New Rows:", len(new_rows))
print("Deleted Rows:", len(deleted_rows))

Tips for Accurate Comparison

1. Sort Your Data First

If File A is sorted by ID and File B is sorted by Name, every line will look different to a simple diff tool. Always sort both files by a unique key (ID, Email) before comparing.

2. Ignore Headers

Ensure you aren't comparing the header row as data.

3. Watch for Formatting

$100 vs 100. 2024-01-01 vs 1/1/24. These are "changes" to a computer, even if the value is the same. Clean/standardize data before comparing.

4. Floating Point Numbers

10.000001 vs 10.0. Computers might see these as different. Use a tool that allows "fuzzy" numeric comparison (tolerance).

Summary

• Visual/Small files: Use Excel Conditional Formatting or an Online Tool.
• Quick Check: Use diff command.
• Data Analysis: Use Python/Pandas.
• Crucial Step: Sort both files before starting!

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Need to see the difference? HappyCSV's Compare Tool visualizes changes instantly in your browser.