# One-hot encode a CSV categorical column One-hot encode a CSV categorical column into binary 0/1 indicator columns. Drop a reference level, cap top categories, and keep or remove the original. ## Run it - **CLI:** `gizza tool one-hot-encoder "city,n Paris,1 Rome,2 Paris,3" 'column=city'` - **Web:** https://gizza.ai/tools/one-hot-encoder/ - **Agents:** machine-readable descriptor (parameters JSON Schema) at https://gizza.ai/tools/one-hot-encoder/tool.json ## Inputs - `data` — CSV data _(field)_ - `column` — Column to encode (name or 1-based number) _(field)_ - `prefix` — Column-name prefix (blank = the column's own name) _(field)_ - `separator` — Prefix separator _(field)_ - `drop` — Reference level to drop _(field)_ - `drop_original` — Remove the original column _(field)_ - `missing` — Blank cells _(field)_ - `max_categories` — Keep only the top N categories (0 = all) _(field)_ - `min_count` — Drop categories seen fewer times than _(field)_ - `other_column` — Add a combined 'other' column _(field)_ - `positive` — Value for a match _(field)_ - `negative` — Value for a non-match _(field)_ - `case_sensitive` — Case-sensitive values _(field)_ - `sort` — Column order _(field)_ - `has_header` — First row is a header _(field)_ - `delimiter` — CSV delimiter _(field)_ ## Output - Encoded CSV (text) ## Query parameters Open the tool pre-filled and auto-run via URL: - `data` — CSV data - `column` — Column to encode (name or 1-based number) - `prefix` — Column-name prefix (blank = the column's own name) - `separator` — Prefix separator - `drop` — Reference level to drop - `drop_original` — Remove the original column - `missing` — Blank cells - `max_categories` — Keep only the top N categories (0 = all) - `min_count` — Drop categories seen fewer times than - `other_column` — Add a combined 'other' column - `positive` — Value for a match - `negative` — Value for a non-match - `case_sensitive` — Case-sensitive values - `sort` — Column order - `has_header` — First row is a header - `delimiter` — CSV delimiter Example: `https://gizza.ai/tools/one-hot-encoder/?data=city%2Cn%0AParis%2C1%0ARome%2C2%0AParis%2C3&column=city&prefix=city&separator=_&drop=none&drop_original=true&missing=zeros&max_categories=0&min_count=0&other_column=true&positive=1&negative=0&case_sensitive=true&sort=alphabetical&has_header=true&delimiter=comma` --- ## About this tool One-Hot Encoder expands a categorical CSV column into a block of binary indicator columns — one per distinct value — where every row carries a **1** in the column matching its category and a **0** in all the others. This is the transformation usually called **one-hot encoding** or **dummy variables**, and it is what most modelling libraries produce from a text column before it can be fed to a regression, a tree ensemble, or a neural net. Pick the column and the tool does the rest: generated columns are named `` (the prefix defaults to the column's own name), and you can drop a **reference level** to avoid the dummy-variable trap, cap the expansion to the **top N** most frequent categories, ignore values seen fewer than N times, keep or remove the source column, and write `true`/`false` (or any other pair) instead of `1`/`0`. ### Worked example CSV: ```csv city,n Paris,1 Rome,2 Paris,3 ``` With column `city` and the defaults, the `city` column is replaced by one indicator per distinct value, in alphabetical order: ```text n,city_Paris,city_Rome 1,1,0 2,0,1 3,1,0 ``` Set **Reference level to drop** to *First category* and the `city_Paris` column disappears — the rows that were Paris are now identified by being 0 in every remaining column, which is the k−1 encoding a linear model needs: ```text n,city_Rome 1,0 2,1 3,0 ``` ### Capping a high-cardinality column Columns like browser, merchant, or ZIP can hold thousands of distinct values, and one column each is rarely useful. **Keep only the top N categories** selects the N most frequent, and **Add a combined 'other' column** collects everything else into a single indicator. With `max_categories = 2`, `other_column` on, the original column kept, and frequency ordering: ```csv browser,hits chrome,10 chrome,20 chrome,30 safari,40 safari,50 lynx,60 ``` ```text browser,hits,browser_chrome,browser_safari,browser_other chrome,10,1,0,0 chrome,20,1,0,0 chrome,30,1,0,0 safari,40,0,1,0 safari,50,0,1,0 lynx,60,0,0,1 ``` ## Limits & edge cases - Categories come from this one input only. There is no separate fit/transform split, so "unseen category" handling does not apply — every value present in the data gets encoded as it is read. - **At most 512 indicator columns** are generated. A higher-cardinality column fails with an explicit error; use *top N categories* or the minimum count to bring it under the limit rather than encoding an ID column. - One column per run. Columns are chosen by header name, or by 1-based number when the header checkbox is off. With no header there is no header row in the output either, so the generated column names are not written. - Indicator columns are always **appended at the end** of each row, in the chosen order, followed by the `other` column and then the `NaN` column when those are enabled. Dropping a reference level applies to the category columns only — the `other` and `NaN` buckets are never the dropped level. - **Top N always selects by frequency**, even when the column order is set to alphabetical or first-seen; the ordering setting only decides how the surviving columns are arranged. Ties are broken by which value appeared first. - Values are trimmed before grouping; with case-sensitivity off they are also lower-cased, and the resulting column is named after the **first spelling seen**. The original cell text is untouched when the source column is kept. - Category values containing the delimiter, quotes, or newlines are quoted in the generated header exactly as CSV requires, so `Paris, FR` becomes the quoted column `"city_Paris, FR"`. ## FAQ
What is one-hot encoding, and when should I use it? One-hot encoding turns a categorical column into several binary columns, one per category, so that a model can use it without inventing an ordering. If you simply numbered categories 1, 2, 3 instead, most models would read that as "3 is bigger than 1", which is meaningless for values like city or browser. Use it for **nominal** columns with a manageable number of distinct values; for high-cardinality columns, cap it with the top-N setting or reach for a compact alternative such as frequency or target encoding.
What is the dummy-variable trap, and which drop option fixes it? With one column per category, the indicators always sum to 1 for every row, so any one of them is perfectly predictable from the others. That perfect collinearity makes a linear regression's coefficients unstable or unsolvable. Dropping one category as a **reference level** fixes it: pick *First category* or *Last category* to get k−1 columns, and rows in the dropped category are then the ones that are 0 everywhere. *Only if the column is binary* drops a level only when there are exactly two categories, which is the common convention for yes/no columns. Tree-based models do not care, so *None* is a fine default for them.
How are blank cells handled? The **Blank cells** setting decides. *Zero in every indicator* (the default) treats a blank as "none of the above", which matches what most encoder libraries do by default. *Own NaN indicator column* adds a `_NaN` column, so missingness itself becomes a feature you can model. *Leave the indicators empty* writes empty cells so the gap stays visible downstream, and *Reject the input* fails with an error rather than silently choosing for you. Blank rows never get a category column of their own unless you choose the separate option.
How do I get true/false or Y/N instead of 1 and 0? Set **Value for a match** and **Value for a non-match** to whatever pair you need — `true`/`false`, `Y`/`N`, or `yes`/`no`. They are written verbatim, so the output plugs straight into a tool that expects booleans rather than integers.
What happens to categories that the top-N or minimum-count limits exclude? By default those rows are simply 0 in every generated column, exactly as if the value had never been listed. Turn on **Add a combined 'other' column** to give them a single shared indicator instead, so the information that the row held *some* rare value is preserved in one column rather than lost or spread across many. The two limits combine: a category must both be seen at least the minimum number of times and survive the top-N cut to get its own column.
Can I encode more than one column at a time? Not in a single run — the tool takes one column per call. To encode several, run it repeatedly, feeding each run's output back in as the next run's input and changing only the column name. Because the generated columns are appended at the end and the source column is removed by default, the results stack cleanly without colliding.
## Related tools - [Frequency-encode a CSV categorical column](https://gizza.ai/tools/frequency-encoder/): Frequency/count encode a CSV categorical column: replace each value with its occurrence count, share, percent, or log-count. Rare-category pooling, replace or append. - [Target-encode a CSV categorical column](https://gizza.ai/tools/target-mean-encoder/): Replace a CSV categorical column with the per-category mean of a numeric target. Supports smoothing, leave-one-out, replace or append output, and unknown handling. - [Rebalance an imbalanced CSV label column](https://gizza.ai/tools/class-rebalancer/): Rebalance an imbalanced CSV label column by seeded random over- or under-sampling of whole rows toward a target class ratio. Reproducible, in-browser, no synthetic rows. - [Base Decoder](https://gizza.ai/tools/base-decoder/): Auto-detect and decode Base16, Base32, Base45, Base58, Base64, and Base85 text, including nested layers, locally in your browser. - [Base32 Encoder / Decoder](https://gizza.ai/tools/base32-codec/): Free Base32 encoder and decoder in your browser — RFC 4648, base32hex, Crockford and z-base-32 variants, hex byte I/O, optional padding. Private, no sign-up.