How to Split Large CSV Files (3 Easy Methods)

Your CSV file is 2 million rows. Excel won't even open it. Your database import times out. Your upload fails because the file is too big.

Time to split that monster into manageable chunks.

Let me show you three ways to split CSV files, when to use each method, and how to avoid common pitfalls.

Why Split CSV Files?

Excel's row limit: 1,048,576 rows maximum. Anything larger just won't open.

File size restrictions: Many systems have upload limits (25MB, 50MB, 100MB). Your 500MB CSV file doesn't fit.

Processing performance: Smaller files process faster. Split into chunks, process in parallel.

Easier management: Would you rather wrangle one 10GB file or ten 1GB files?

Sharing portions: Send someone just the rows they need, not the entire dataset.

Method 1: Split by Number of Rows

This is the most common approach - break your file into chunks of X rows each.

Example:

• Original: 1,000,000 rows
• Split into: 10 files of 100,000 rows each
• Each file gets: Headers + 100,000 data rows

How to Do This

Option A: Use a Split Tool

1. Upload your large CSV
2. Specify rows per file (e.g., 100,000)
3. Download ZIP containing all split files
4. Each file includes headers automatically

→ Split CSV by rows

Option B: Command Line (Linux/Mac)

# Split into files of 100,000 lines each
split -l 100000 largefile.csv chunk_

# Add headers to each chunk (requires a bit more work)
head -1 largefile.csv > headers.txt
for file in chunk_*; do
  cat headers.txt $file > with_headers_$file.csv
done

Option C: Python Script

import pandas as pd

chunk_size = 100000
df = pd.read_csv('largefile.csv', chunksize=chunk_size)

for i, chunk in enumerate(df):
    chunk.to_csv(f'split_part_{i+1}.csv', index=False)

When to Use Row Splitting

Perfect for:

• Overcoming Excel's row limit
• Working around upload size restrictions
• Parallel processing workflows
• Splitting chronological data by time period

Example: You have a year's worth of transaction logs (12M rows). Split into 12 files of 1M rows each (one per month).

Method 2: Split by Column Value

Instead of arbitrary row counts, split based on actual data in a column.

Example:

• Column: "Country"
• Split result: USA.csv, Canada.csv, UK.csv, Mexico.csv

Each file contains only rows where Country matches that value.

How to Do This

Using a Tool:

1. Upload CSV
2. Select column to split by (e.g., "Country")
3. Tool creates one file per unique value
4. Download ZIP with all files

→ Split CSV by column

Using Excel (Small Files Only):

1. Apply Auto-Filter to your column
2. Filter by first value
3. Copy visible rows to new sheet
4. Save as CSV
5. Repeat for each value

Tedious, but works for occasional use.

Using SQL (If Data in Database):

-- Export each region to separate file
SELECT * FROM table WHERE region = 'North' INTO OUTFILE 'north.csv';
SELECT * FROM table WHERE region = 'South' INTO OUTFILE 'south.csv';

When to Use Column Splitting

Perfect for:

• Distributing data by region/territory
• Separating by category/type
• Creating department-specific files
• Grouping by customer/ account

Example: You have a customer list of 100K rows. Split by "Sales Rep" to create separate files for each sales person to manage their own leads.

Watch Out for High Cardinality

High cardinality = lots of unique values in a column.

If you split by "Customer ID" and have 50,000 customers, you'll get 50,000 files. That's not useful.

Better choices for splitting:

• Country (maybe 20-50 values)
• Region (5-10 values)
• Category (10-30 values)
• Year/Month (12-24 values for yearly data)

Bad choices:

• Email (unique per row)
• Transaction ID (unique per row)
• Timestamp (thousands of unique values)

Method 3: Manual Split in Excel (Last Resort)

If you're desperate and have no other tools:

1. Open CSV in Excel (if it fits)
2. Select first 100,000 rows
3. Cut (Ctrl+X)
4. Paste into new workbook
5. Save as CSV (part_1.csv)
6. Repeat with next 100,000 rows

Why this is terrible:

• Time-consuming
• Error-prone
• Excel might crash with large files
• You might miss rows or duplicate some

When to use this: Never, unless you have literally no other option.

File Naming Best Practices

For Row Splits

Good:

data_part_001.csv
data_part_002.csv
data_part_003.csv

Better:

transactions_2024_part_01_rows_1-100000.csv
transactions_2024_part_02_rows_100001-200000.csv

Why: Numbered parts, clear ranges, sortable.

Bad:

split1.csv
split2.csv
newfile.csv

Why: Not descriptive, hard to track order.

For Column Splits

Good:

customers_USA.csv
customers_Canada.csv
customers_UK.csv

Better:

2024_customers_USA.csv
2024_customers_Canada.csv

Why: Includes context (year), clear meaning.

Rules:

• Use underscores, not spaces
• Include date if relevant
• Be descriptive but concise
• Use consistent format across all files

Don't Forget Headers!

The #1 mistake when splitting: forgetting to include headers in each file.

Bad split:

File 1: John,john@email.com,555-1234
File 2: Sarah,sarah@email.com,555-5678

Good split:

File 1: Name,Email,Phone
        John,john@email.com,555-1234

File 2: Name,Email,Phone
        Sarah,sarah@email.com,555-5678

Each file needs headers for:

• Excel/Sheets to display column names
• Imports to map columns correctly
• Humans to understand what each column is

Most split tools do this automatically. If you're using command-line or manual methods, double-check.

After Splitting: Verification

Before you archive your original file or send split files to others, verify:

Row count adds up:

Original: 1,000,000 rows
Part 1: 100,000 rows
Part 2: 100,000 rows
...
Part 10: 100,000 rows
Total: 1,000,000 ✓

No data loss:

• Spot-check: Is customer #500 in the right file?
• Check first and last rows of each part
• Verify unique identifiers (IDs) are all present

All files are valid CSVs:

• Open each in text editor - does it look right?
• Try opening in Excel - does it parse correctly?
• Check for weird characters or corruption

Headers present:

• Every file should have the same header row
• Column order should match original

Common Pitfalls

Pitfall 1: Splitting Mid-Row

If your CSV has multi-line fields (text with line breaks inside), splitting at a specific line count might break rows apart.

Example:

Name,Address
"John Smith","123 Main St
Apartment 5"

If you split after line 2, you'll break John's address.

Solution: Use a tool that understands CSV structure, not just line counts.

Pitfall 2: Inconsistent Encoding

Original file is UTF-8. You split it. Some files save as ANSI. Now you have encoding mismatches.

Solution: Verify all split files have the same encoding as the original (usually UTF-8).

Pitfall 3: Losing Track of Order

You split into 50 files. Now you can't remember which one has what data.

Solution:

• Use descriptive filenames
• Keep a manifest file listing what each contains
• Put all split files in one folder with README

Pitfall 4: Not Keeping Original

You split the file, delete the original to save space, then realize you need to re-split differently.

Solution: Always keep the original master file. Archive it if needed, but don't delete.

Use Cases

Use Case 1: Excel Row Limit

Problem: CSV has 2M rows, Excel maxes at 1M.

Solution: Split into 2 files of 1M rows each. Process separately in Excel.

Use Case 2: Upload Size Limit

Problem: Database import tool has 50MB limit, your CSV is 200MB.

Solution: Split into 4 files of 50MB each. Import sequentially.

Use Case 3: Parallel Processing

Problem: Processing 10M rows takes 5 hours.

Solution: Split into 10 files of 1M rows. Process in parallel on multiple machines. Done in 30 minutes.

Use Case 4: Regional Distribution

Problem: You have global customer data. Each region needs their own file.

Solution: Split by "Region" column. Send each regional file to respective team.

Merging Split Files Back Together

Eventually you might need to recombine split files.

Quick method:

1. Put all split files in one folder
2. Use merge tool
3. Download combined file

→ Merge CSV files

Command line:

# Combine all split files (headers only from first file)
cat split_part_*.csv > combined.csv

Note: This includes headers from every file. You'll need to remove duplicates manually or use a tool that handles this.

The Bottom Line

Choose your split method based on need:

• Row count: Overcome size limits, parallel processing
• Column value: Distribute by category/region
• Manual: Never (use a tool instead)

Before splitting:

• Backup original file
• Decide on split criteria
• Plan file naming convention

After splitting:

• Verify row counts
• Check for data loss
• Confirm headers in all files
• Test that files are valid CSVs

Large CSV files don't have to be scary. Split them into manageable pieces and suddenly that "impossible" task becomes totally doable.

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Split your CSV files easily: HappyCSV's split tools handle both row-based and column-based splitting, preserve headers automatically, and package everything in a ZIP for easy download.