Empty Rows and Columns
Empty rows and columns are the most common CSV formatting issue. They appear when you delete data in a spreadsheet but leave the row or column structure intact.
Why it matters::
- Import tools often reject files with empty rows
- Empty columns waste space and confuse users
- Database imports can fail or create null records
- Analytics tools may miscount your data
Example::
```
Name,Email,Age
John Doe,john@example.com,28
,,
Jane Smith,jane@example.com,34
```
That third row (all blank) will cause problems when importing. The same issue happens with completely empty columns that have a header but no data.
Hidden Whitespace
Trailing or leading spaces are invisible but destructive. They make values that look identical behave differently.
Why it matters::
- `"John"` and `"John "` are treated as different values
- VLOOKUP and matching functions fail
- You get false duplicates in your data
- Filters and searches don't work correctly
How it happens::
- Copy-pasting from websites or PDFs
- Manual data entry with extra spaces
- Exporting from systems with poor formatting
This is one of the hardest issues to spot because spaces are invisible in most spreadsheet viewers. You need to check the underlying CSV text or use a cleaning tool.
Inconsistent Date Formats
CSV files don't have a standard date format. Different systems export dates differently, and mixing formats causes chaos.
Common date format problems::
- `12/01/2024` (Is this January 12th or December 1st?)
- `2024-01-12` (ISO format)
- `Jan 12, 2024` (Human-readable but hard for computers)
- `1/12/24` (Ambiguous short format)
When you import a CSV with mixed date formats, some dates get parsed correctly while others become text or get misinterpreted. This corrupts time-series data and breaks date-based calculations.
The safest approach is standardizing all dates to ISO 8601 format (YYYY-MM-DD) before importing.
Duplicate Rows
Duplicate entries usually come from multiple imports, copy-paste errors, or merging datasets without proper deduplication.
Why duplicates are dangerous::
- They inflate totals and skew analytics
- Duplicate customers get multiple emails
- Financial reports show incorrect revenue
- Database constraints may reject the import entirely
Example of duplicates::
```
Order ID,Customer,Amount
1001,John Doe,250
1002,Jane Smith,180
1001,John Doe,250
```
Order 1001 appears twice with identical data. This could double-count revenue or cause errors when the Order ID field is supposed to be unique.
Malformed Number Formats
Numbers exported from different systems often include currency symbols, thousands separators, or inconsistent decimal places.
Problem examples::
- `$1,234.56` (currency symbol and comma)
- `1.234,56` (European format with reversed separators)
- `1234.5600` (unnecessary trailing zeros)
Most import tools expect plain numbers: 1234.56. When they encounter formatted numbers, they either fail or treat the value as text instead of a number, breaking calculations and summaries.
Ready to put this into practice?
Use our free CSV cleaning tools to fix these issues automatically: