Data Normalizer
Feature-scale the numeric columns of a CSV with min-max, z-score (standardization), or robust (median/IQR) normalization — choose the method, range, and columns. Runs entirely in your browser, nothing is uploaded.
Normalize the numeric columns of a CSV
Paste a CSV and this tool feature-scales its numeric columns locally in your browser — nothing is uploaded. Pick the normalization method that fits your data:
- Min-max — linearly rescale each column into a target range (default
0–1) using its own minimum and maximum. Good for bounding features to a fixed interval. - Z-score — standardize each column to mean
0and unit standard deviation. Choose population (ddof = 0) or sample (ddof = 1, Bessel-corrected) stddev. - Robust — center on the median and scale by the interquartile range (IQR, the 25th–75th percentile spread). Resistant to outliers; toggle centering and scaling independently.
Only numeric columns are scaled — a column counts as numeric when every present value parses as a finite number. Text columns are copied through unchanged, and blank cells stay blank (they're excluded from the column's statistics).
Worked example
With the default Min-max method scaling to 0–1, this input:
name,height,weight
Alice,150,50
Bob,160,60
Carol,170,70
becomes:
name,height,weight
Alice,0,0
Bob,0.5,0.5
Carol,1,1
height runs 150–170, so 160 sits halfway → 0.5; weight runs 50–70,
so 60 → 0.5. The name column is text, so it's copied through untouched.
FAQ
What is the difference between min-max, z-score, and robust scaling?
Min-max rescales a column into a fixed interval (like 0–1) based on its
minimum and maximum, so outliers can compress the rest of the values. Z-score
(standardization) shifts each column to mean 0 and divides by its standard
deviation, giving unbounded values centered on zero. Robust scaling uses the
median and interquartile range instead of the mean and stddev, so a few extreme
values barely move the result — pick it when your data has outliers.
Which columns get normalized?
By default every numeric column. Type a comma-separated list under Columns
to scale to restrict it — use header names (like height,weight) when the first
row is a header, or 1-based indexes (like 2,3). Columns you don't list, and any
text columns, are copied through unchanged.
How are blank cells and text columns handled?
A blank (empty or whitespace-only) cell stays blank and is left out of the column's
minimum, maximum, mean, median, and IQR calculations. A column with any non-numeric
present value is treated as text and copied verbatim — NaN and inf count as
non-numeric, so a column containing them is not scaled.
What do range, ddof, and the robust toggles do?
Range minimum / maximum set the target interval for min-max (default 0–1;
the minimum must be less than the maximum). Ddof picks the z-score standard
deviation: 0 divides by n (population), 1 divides by n − 1 (sample). For
robust scaling, Subtract the median and Divide by the IQR each toggle one
step — turn either off to center only or scale only.
What happens when a column is constant or has zero spread?
If a column's values are all equal there is nothing to spread out: min-max returns
the range minimum, z-score returns 0 (its standard deviation is zero), and robust
scaling divides by 1 instead of a zero IQR (so with centering on you get 0).
Is my data uploaded anywhere?
No. The whole computation runs locally with WebAssembly; your CSV never leaves your browser.
Limits & edge cases
- Scaling needs numbers: a column with any non-numeric present value is treated as text and copied unchanged. Use a separate cleanup step for categorical data.
- Scaled values are rounded to 6 decimal places and printed minimally (
1.0→1). - Rows may have different column counts; short rows are padded with empty (blank) cells to the widest row before scaling.
- 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 scaled like any other.
- Range, ddof, and the robust toggles only affect their own method; e.g. changing the range has no effect under z-score or robust.
- 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 data-normalize "name,height,weight
Alice,150,50
Bob,160,60
Carol,170,70"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/data-normalize/?input=name%2Cheight%2Cweight%0AAlice%2C150%2C50%0ABob%2C160%2C60%0ACarol%2C170%2C70&method=min_max&header=true&delimiter=comma&columns=height%2Cweight&range_min=0&range_max=1&ddof=0&with_centering=true&with_scaling=trueMachine-readable descriptor: tool.json — title + parameters JSON Schema for agents.