What This Tool Does

This tool analyzes your CSV file and provides row count statistics. It counts total rows, identifies blank or empty rows, and calculates per-column fill rates (percentage of non-empty values). Use it to assess data quality and completeness before processing or importing.

Statistics Provided

Total rows: Count of all data rows (excluding header).

Blank rows: Rows where all columns are empty. Often artifacts from copy-paste or export errors.

Per-column fill rate: For each column, the percentage of rows with non-empty values.

Null count per column: Number of empty cells in each column.

Unique count: Number of distinct values in each column.

Exportable report: Download statistics as CSV for further analysis or documentation.

Example Output

Input CSV:

name,email,phone,department
Alice,[email protected],555-0100,Engineering
Bob,[email protected],,Marketing
Charlie,,,Sales

Diana,[email protected],555-0103,HR

Statistics:

Total rows: 5
Blank rows: 1

Column Statistics:
name       - 80% fill (4/5), 1 null
email      - 60% fill (3/5), 2 nulls
phone      - 40% fill (2/5), 3 nulls
department - 80% fill (4/5), 1 null

When to Use This

Data quality assessment: Before importing data, check how complete each column is.

Identify problematic columns: Columns with low fill rates may indicate export issues or optional fields.

Find blank rows: Detect empty rows that could cause import errors or skew analysis.

Dataset documentation: Include fill rate statistics in data dictionaries or documentation.

Migration planning: Understand data completeness before migrating to a new system with required fields.

Understanding Fill Rate

Fill rate is the percentage of rows with non-empty values in a column:

100% fill: Every row has a value. Ideal for required fields.

80-99% fill: Mostly complete. Some missing values may be acceptable.

50-79% fill: Moderate completeness. Investigate why values are missing.

Below 50% fill: Sparse data. Column may be mostly unused or poorly populated.

0% fill: Completely empty column. Consider removing it.

What Counts as Empty

The following are considered empty/null:

• Completely blank cells
• Cells with only whitespace
• Explicit "null" or "NULL" text (optional)
• Empty quoted strings ("")

Zero (0) is NOT empty — it's a valid numeric value.

Use Cases

CRM data audit: A sales team exports their contacts and discovers phone numbers are only 30% complete. They launch a data enrichment project.

E-commerce product audit: An online retailer finds that 40% of products are missing descriptions. They prioritize filling this gap.

Survey response analysis: A researcher checks response rates per question to identify which questions were skipped most often.

Limitations

Large files: Works best with files under 50MB. Very large files may cause slow analysis.

Deep analysis: This tool provides basic statistics. For advanced analytics (mean, median, distributions), use CSV Column Statistics.

Frequently Asked Questions

Does the header row count as a row?

No. Row counts exclude the header row. Only data rows are counted.

Can I export the statistics?

Yes. Download the statistics as a CSV file for use in reports or further analysis.

How are blank rows different from null values?

Blank rows have ALL columns empty. Null values are empty cells within otherwise populated rows. Blank rows are often errors; null values may be legitimate missing data.