⬜CSV Null Value Checker

Find Null and Missing Values
by Column in Any CSV

Upload a CSV and instantly see fill rate and completeness for every column including hidden missing data disguised as "N/A", "NULL" or "none". Fill or drop missing values and download the cleaned file.

✓ CSV files only✓ Detects NULL / N/A / none text✓ Fill rate per column✓ No upload to server✓ Free forever
📂

Drop your CSV file here

CSV files only your file never leaves your browser

Supports any CSV regardless of size. No row limit.

How to use

How to Find Null Values in a CSV File

This tool checks every cell in your CSV for missing data, including hidden placeholder text that looks like a value but represents nothing. No formulas, no pandas, no Excel COUNTBLANK needed.

1
📂

Upload your CSV

Click or drag your CSV file onto the upload area. Only CSV files are accepted. Your file is read locally nothing is sent to any server.

2
📊

Check the overview

See overall completeness and a fill rate bar for every column. Green is complete, amber is mostly complete, red needs attention.

3
🔎

Toggle null-like text

Enable "Detect null-like text" to also catch cells containing NULL, N/A, none, NaN or "-" values that look filled but mean nothing.

4
🧹

Fill, drop, download

In Clean Data, fill missing values with zero, mean, mode or custom text or drop incomplete rows. Then download the cleaned CSV.

The hidden problem

Why "100% Filled" Columns Often Aren't

Most null checkers including basic spreadsheet formulas like COUNTBLANK only catch cells that are literally empty. But real-world CSVs exported from CRMs, forms, APIs and legacy systems are full of cells that contain text specifically meaning "no value": NULL, N/A, none, NaN, -, undefined.

A column showing 100% fill rate by a basic check can still be functionally empty in 15% of rows if those rows contain the literal text "N/A". When that column is loaded into Power BI as a numeric measure, those text values either break the import or get silently coerced to zero corrupting your averages without any error message. The "Detect null-like text" toggle in this tool reveals exactly this gap, recalculating fill rates in real time so you see the true picture.

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Truly empty

Cell has zero characters the obvious case every tool catches

␣

Whitespace only

Looks empty visually but contains a space or tab character

🏷️

Null-like text

Cells containing NULL, N/A, none, NaN, -, undefined and similar

Why this matters

Why Missing Values Break Downstream Systems

A SQL import that hits a null in a NOT NULL column rejects the row or the whole file depending on the database. A Power BI measure summing a column with nulls returns a blank. A model trained on incomplete data either errors out or learns from corrupted patterns. The fill rate percentage tells you not just whether the problem exists, but how severe it is a column at 99% has a trivial gap, while a column at 45% has a structural collection problem upstream.

Four ways to handle missing values, all available in the Clean Data tab

0️⃣

Fill with Zero

Replaces missing values with 0. Best for numeric counts or quantities where missing genuinely means none.

📐

Fill with Mean

Replaces missing values with the column average. A standard imputation technique for continuous numeric data.

🏷️

Fill with Mode

Replaces missing values with the most common value in that column. Works for any data type, ideal for categorical fields.

✏️

Custom or Drop

Type any custom fill text (e.g. "Unknown"), or drop rows entirely either for one column or any row with any gap.

FAQ

Frequently Asked Questions

What exactly counts as a missing value? +
By default: cells that are completely empty or contain only whitespace. Enable "Detect null-like text" to also count cells containing NULL, N/A, n/a, none, NaN, -, --, #N/A, undefined, nil, unknown, TBD and similar placeholder text case-insensitively.
Why did my completeness score change when I toggled "Detect null-like text"? +
That's the point. If your data was exported from a system that writes literal "N/A" or "NULL" text instead of leaving cells empty, the basic check sees those as filled. Toggling the option recalculates every fill rate to reveal the true completeness often surfacing problems that were completely invisible before.
Can I fill different columns with different strategies? +
Yes. Select a column from the dropdown in Clean Data, choose a fill method just for that column, then select the next column and repeat. Each action applies only to the currently selected column.
What happens if I fill a text column with "mean"? +
The "Fill with mean" option only appears for columns where most values are numeric. For text columns, use "Fill with most common value" (mode) or type a custom value instead.
Can I undo my changes? +
Yes. Click Reset at any time to restore the file exactly as it was when uploaded. This works for any number of fill or drop actions performed in the same session.
How is this different from the TOOLBeans Data Profiler? +
This tool is purpose-built for one job: finding and fixing missing values in CSV files, including hidden null-like text that basic checks miss, with per-column fill strategies (zero, mean, mode, custom). The Data Profiler is broader it covers duplicates, type mismatches, outliers and an overall quality score across CSV, Excel, JSON and API data, but does not offer the null-like text detection or per-column fill workflow this tool provides.
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