Missing Value Imputer
Fill blank cells in a CSV with mean, median, most-frequent, a constant, or KNN imputation — choose the columns and what counts as missing. Runs entirely in your browser, nothing is uploaded.
Fill missing values in a CSV
Paste a CSV with blank or NA cells and this tool fills them in locally in your
browser — nothing is uploaded. Pick the imputation strategy that fits your
data:
- Mean — replace blanks in a numeric column with the column average.
- Median — replace blanks with the middle value; robust to outliers.
- Most frequent — replace blanks with the column's most common value; works on text (categorical) columns too.
- Constant — write a fixed value (like
0orUnknown) into every blank. - KNN — estimate each blank from its k nearest rows using a nan-euclidean distance over the numeric columns, averaged uniformly or by inverse distance.
A cell counts as missing when it is empty after trimming, or equals one of the
extra markers you list under missing markers (for example NA, null, ?).
Mean, median, and KNN only touch numeric columns (a column whose every present
value parses as a number); most-frequent and constant apply to any column.
Worked example
With the default Mean strategy, this input:
name,age,city
Alice,30,NYC
Bob,,LA
Carol,40,
becomes:
name,age,city
Alice,30,NYC
Bob,35,LA
Carol,40,
age is numeric, so Bob's blank is filled with the mean of 30 and 40 — 35.
The blank city for Carol is left untouched because city is a text column and
mean/median only impute numeric data; switch to Most frequent or Constant
to fill it.
FAQ
Which columns get imputed?
By default every applicable column. Type a comma-separated list under Columns to
impute to restrict it — use header names (like age,income) when the first row
is a header, or 1-based indexes (like 2,3). Columns you don't list are copied
through unchanged.
What counts as a missing value?
A cell is missing when it's empty or whitespace-only after trimming. To also treat
sentinel strings as missing, list them under Extra missing markers —
NA,null,? for example. Matching is exact and case-sensitive, so NA is missing
but na is not unless you add it too.
How does the mean/median differ from most-frequent and constant?
Mean and median are numeric: they only fill columns whose present values are all numbers, and they skip text columns entirely. Most frequent picks the most common existing value (ties go to the first one seen) and works on any column, including categories. Constant ignores the data and writes whatever you put in Fill value into every blank.
How does KNN imputation work here?
For each missing numeric cell, the tool measures the distance from that row to
every other row that has a value in the column, using a nan-euclidean metric over
the numeric columns (it scales by the number of shared present features). It then
averages the n_neighbors closest rows — a plain average with Uniform weights,
or an inverse-distance weighted average with Distance. Rows with no comparable
neighbour fall back to the column mean.
Is my data uploaded anywhere?
No. The whole imputation runs locally with WebAssembly; your CSV never leaves your browser.
Limits & edge cases
- Mean, median, and KNN need numbers: a column with any non-numeric present value is treated as text and left unchanged by those strategies. Use most-frequent or constant for categorical data.
NaNandinfare not valid numbers here — a column containing them is treated as text, not numeric.- Rows may have different column counts; short rows are padded with empty (missing) cells to the widest row before imputing.
- Turn off First row is a header for files with no header row; then column selection must use 1-based indexes and row 1 is imputed like any other.
- KNN with no usable neighbour (e.g. every other row also blank in that column) falls back to the column mean, and to nothing if the column is entirely empty.
- Everything runs in memory, so very large files are bounded by your browser's available memory.
Developer & Automation Access
Run it from the terminal
Same engine as this page, headless — via the gizza CLI:
gizza tool missing-value-imputer "name,age,city
Alice,30,NYC
Bob,,LA
Carol,40,"New to the CLI? Get gizza →
Open it by URL
Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:
https://gizza.ai/tools/missing-value-imputer/?input=name%2Cage%2Ccity%0AAlice%2C30%2CNYC%0ABob%2C%2CLA%0ACarol%2C40%2C&strategy=mean&header=true&delimiter=comma&columns=age%2Ccity&na_tokens=NA%2Cnull%2C%3F&fill_value=0&n_neighbors=5&weights=uniformMachine-readable descriptor: tool.json — title + parameters JSON Schema for agents.