Find Null and Missing Values in CSV
Upload any CSV or Excel file and instantly see every null value and missing cell by column. Fill nulls or drop incomplete rows with one click, then download your cleaned dataset.
CSV & Excel Data Analyzer
Find duplicates, nulls & errors · Clean & export · Auto dashboard
Drop your data file here
or click to browse CSV, Excel (.xlsx / .xls) or JSON
Related Tools
Free tools that complement your data workflow.
Free CSV and Excel Data Analyzer Find Duplicates, Nulls and Errors Instantly
Upload any CSV or Excel file to instantly find duplicate rows, null values, type mismatches and data quality issues. The Clean Data tab lets you remove duplicates, fill nulls and standardize headers in one click, then download the cleaned file. The Dashboard tab auto-generates charts from your data. No Python, no SQL, no formulas required.
Why Missing Values Break Downstream Systems
Missing values cause silent failures in almost every data system. A SQL import that encounters a null in a NOT NULL column will reject the entire row or the entire file depending on the database. A Power BI measure that tries to SUM a column with nulls will return a blank or incorrect result. A machine learning model trained on data with nulls will either error out or silently learn from incomplete information.
The challenge is that missing values are not always obvious. A CSV might look complete in Excel because empty cells look the same as cells containing spaces. A column might have 5,000 rows of valid data and 200 rows of blanks at the bottom that are easy to miss when scrolling. This tool scans every single cell and gives you exact counts by column, so nothing is hidden.
The fill rate percentage per column tells you not just whether nulls exist but how severe the problem is. A column at 99% fill rate has a trivial problem probably a few data entry gaps. A column at 45% fill rate has a structural problem the data is not being collected consistently at the source and needs to be fixed upstream before the data is useful for analysis.
Three Ways to Handle Null Values
Removes any row that has at least one null value. Best when you need a completely clean dataset and can afford to lose some records. Use when nulls represent genuinely bad data.
Replaces every null with 0. Best for numeric columns where a missing value genuinely means zero such as a count of transactions or a quantity field.
Replaces each null with the average value of that column. A statistical imputation technique that preserves column distribution. Best for continuous numeric variables where the missing value could reasonably be average.