# Weighted Random Sampler Draw weighted random samples from CSV or JSON rows, with or without replacement. Seeded, reproducible, and browser-local. ## Run it - **CLI:** `gizza tool weighted-random-sampler "name,weight Alice,5 Bob,1 Carol,3 Dan,1" 'weight_field=weight'` - **Web:** https://gizza.ai/tools/weighted-random-sampler/ - **Agents:** machine-readable descriptor (parameters JSON Schema) at https://gizza.ai/tools/weighted-random-sampler/tool.json ## Inputs - `data` — Dataset (CSV or JSON array) _(field)_ - `format` — Format _(field)_ - `weight_field` — Weight field (column name/index or JSON key) _(field)_ - `n` — Sample size (rows to draw) _(field)_ - `replacement` — Sample with replacement (allow repeats) _(field)_ - `seed` — Seed (change for a different draw) _(field)_ - `header` — First row is a header (CSV) _(field)_ - `delimiter` — Delimiter (CSV) _(field)_ ## Output - Sampled rows (text) ## Query parameters Open the tool pre-filled and auto-run via URL: - `data` — Dataset (CSV or JSON array) - `format` — Format - `weight_field` — Weight field (column name/index or JSON key) - `n` — Sample size (rows to draw) - `replacement` — Sample with replacement (allow repeats) - `seed` — Seed (change for a different draw) - `header` — First row is a header (CSV) - `delimiter` — Delimiter (CSV) Example: `https://gizza.ai/tools/weighted-random-sampler/?data=name%2Cweight%0AAlice%2C5%0ABob%2C1%0ACarol%2C3%0ADan%2C1&format=csv&weight_field=weight&n=2&replacement=true&seed=42&header=true&delimiter=comma` --- ## About this tool **Weighted Random Sampler** draws a random subset of rows from a dataset where some rows should appear more often than others. Each row carries a **weight** (a non-negative number in a column or JSON key), and the sampler picks rows with probability **proportional to that weight** — a row with weight 5 is five times as likely to be drawn as a row with weight 1. It works on **CSV** text or a **JSON array of objects**, and every draw is **reproducible**: the same seed and inputs always produce the same sample. Use it to build weighted lotteries and giveaways, to over- or under-sample classes when preparing training data, to pick survey respondents proportional to a population weight, or to simulate a weighted draw for testing. ### Worked example Input CSV (weights in the `weight` column): ``` name,weight Alice,5 Bob,1 Carol,3 Dan,1 ``` Drawing **2 rows without replacement**, `seed=42`, gives: ``` name,weight Alice,5 Carol,3 ``` Alice and Carol carry the most weight, so with this seed they win the draw. Change the seed for a different sample, or turn **replacement** on to let a heavy row be drawn more than once (then the sample size can exceed the number of rows). ### With vs. without replacement - **Without replacement** (default) — each row is drawn at most once, so the sample size can't exceed the number of rows that have a positive weight. Rows come back in their original order. Uses the Efraimidis–Spirakis weighted scheme. - **With replacement** — each of the `n` draws is independent, so the same row can appear several times and `n` may be larger than the dataset. Rows come back in draw order. ### Limits & edge cases - Weights must be **non-negative numbers**. A row with weight `0` is never drawn; a non-numeric weight is an error that names the offending row. - If **all** weights are zero, there is nothing to sample and the tool errors. - Without replacement, asking for more rows than have a positive weight is an error. - CSV: the header row (when enabled) is preserved and used to resolve a named weight field; the chosen delimiter is used for both input and output. JSON: the input must be an array of objects and the output is a JSON array of the selected objects. - Everything runs locally in your browser — the dataset never leaves your machine. ## FAQ
How are the weights turned into probabilities? Each row's chance of being drawn is its weight divided by the sum of all weights. So in `A=5, B=1, C=3, D=1` (total 10), A has a 50% chance per draw, C 30%, and B and D 10% each. You don't need the weights to add up to 1 or 100 — any non-negative numbers work, and they're normalized for you.
What's the difference between sampling with and without replacement? **Without replacement** (the default) draws each row at most once, so you can't ask for more rows than exist, and the result keeps the input order. **With replacement** treats every draw independently, so the same row can be picked multiple times and you can draw more rows than the dataset contains — useful for bootstrap resampling or weighted dice-style draws.
Is the sample reproducible? Yes. Sampling uses a small deterministic PRNG seeded by the **seed** field, so the same dataset, size, and seed always produce the exact same rows. Change the seed to get a different draw while keeping everything else fixed.
Can I use it with JSON instead of CSV? Yes. Set **Format** to `json` and paste a JSON array of objects, then give the **weight field** as the object key holding the weight (e.g. `w`). The result is a JSON array containing the full selected objects. Weights may be JSON numbers or numeric strings like `"2.5"`.
What happens to rows with a zero or missing weight? A weight of `0` means the row is never selected, but it stays in your dataset and counts toward nothing. A missing weight field or a non-numeric value is treated as an error that tells you which row is at fault, so you can fix the data rather than get a silently skewed sample.
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