Find and report empty cells and missing values
Missing values can silently skew analysis results or break downstream processes. This analyzer scans every column for nulls, empty strings, and common placeholders like "N/A" or "n/a"then produces a completeness report with per-column and per-row breakdowns.
Initializing in your browser…
A dataset feels incomplete and you need to quantify exactly where the gaps are before cleaning.
Input
survey.csv (5,000 rows, 20 columns)
Report
income: 31% missing region: 0% 42 rows entirely blank after column 6 Columns with any nulls: 7 of 20
A per-column completeness breakdown plus row-level gap patterns tells you whether to impute, drop, or go back to the source, and reveals structural issues like a block of rows truncated mid-file. Guessing at missingness is how biased analyses happen.
Missing values can silently skew analysis results or break downstream processes. This analyzer scans every column for nulls, empty strings, and common placeholders like "N/A" or "n/a"then produces a completeness report with per-column and per-row breakdowns.
Understand how complete your dataset is before running statistical models that assume no missing values.
Verify that data deliveries from third parties meet agreed completeness thresholds.
Identify columns with too many gaps to migrate into a new system with NOT NULL constraints.
Decide which columns need imputation and which strategy (mean, median, forward-fill) to apply based on gap patterns.
Beyond simple null counting, the tool recognizes a configurable set of missing-data markers: empty strings, "NA""N/A""null""none""-"and custom values you specify. It calculates completeness percentages per column, identifies rows with the most gaps, and highlights patterns, such as a column that is fully populated for one date range but empty for another, suggesting a schema change midway through data collection.
The visual summary uses heat-map-style coloring so you can spot problem areas instantly across dozens of columns.
By default: empty cells, "NA""N/A""null""none"and "-". You can add or remove markers to match your dataset's conventions.
Yes. The tool highlights systematic patterns like columns that become empty after a certain row, suggesting schema changes or data collection issues.
Yes, the analyzer processes files with hundreds of thousands of rows efficiently in your browser.
Rows and columns are parsed and transformed in memory in your browser. No record ever reaches a server.