How many bins should I use?

Sturges' rule: 1 + log₂(n). For 100 values that's ~8 bins; for 1000 it's ~11. The tool defaults to a sensible guess based on your data size; the slider lets you tune. Too few bins hides structure, too many shows noise as structure.

Should I use a linear or log scale?

Linear for symmetric or near-uniform distributions. Log for heavily-skewed data (file sizes, request latencies, income) — otherwise everything bunches into the first bin. The tool offers both via a toggle.

Why does the histogram look so different with a different bin count?

Bin choice is a smoothing parameter. The underlying distribution is the same; you're just changing the resolution. Try a few bin counts to see the data's robust shape vs binning artefacts.

binning · distribution · local · live

Bin numbers into a horizontal histogram

Paste numbers, drag the bin slider, and see the frequency distribution as horizontal bars with counts and ranges. Runs entirely in your browser.

Histogram from Numbers

values

Bins

selected

Range

1 – 92

w 9.1

Status

ready

live

input · numbers

output · histogram

[1, 10.1)

[10.1, 19.2)

[19.2, 28.3)

[28.3, 37.4)

[37.4, 46.5)

[46.5, 55.6)

[55.6, 64.7)

[64.7, 73.8)

[73.8, 82.9)

[82.9, 92]

Quick Start

How to read a histogram

Each bar shows how many values fall inside a fixed-width range. Wider bins smooth the shape; narrower bins reveal detail.

01
Paste numbers
Separate with commas, spaces, semicolons, or newlines.
Inputs are treated as UTF-8 throughout the pipeline and validated client-side as you type — nothing dispatches until the value is well-formed.
inpututf-8client-sidevalidated
input · textn · set
```
1, 3, 4, 7, 8, 9
```
loaded · parsedclient-side
02
Pick bin count
5-50 bins via the slider. Rule of thumb: √n.
Each toggle re-computes through a memoised hook, so flipping options never resets your input and the UI stays snappy on large inputs.
binsoptionslivememoisedtoggles
bins · memoised10 · live
```
√n ≈ 6
```
options set · memoisedlive
03
Read the shape
Peaks reveal modes; long tails reveal skew.
Randomness — where used — comes from crypto.getRandomValues, never Math.random. Heavy work sits in useMemo.
shapecomputeinstantlocaldeterministic
shape · instantright-skew · computed
```
▆▇▅▃▂▁
```
computed · instantinstant
04
Copy or download
Plain-text report of each bin range and count.
Copy writes through navigator.clipboard and downloads use a Blob — zero network traffic. Same input plus same options always yield the same bytes.
exportcopydownloadzero-networkreproducible
ok · copiedready · copied
```
Copied
```
shipped · ready to shipsuccess

Pro tips

Pro tips for everyday utilities

Reach for crypto.getRandomValues

Cryptographically random for IDs, tokens, salts. Math.random is for animation and games, not security.

Sort IDs lexicographically

UUID v4 has no time ordering. Use ULID or UUID v7 if you need sorted IDs.

Mock data with seeds

Faker-style libraries can be seeded so tests are reproducible. Embed the seed in the fixture file.

Reference cards beat memory

Cheatsheets for Vim, Git, Docker, Linux signals are faster than man pages when you’re mid-flow.

Browser APIs vary

Touch events, gamepads, and clipboard differ across vendors. Feature-detect before use.

Persist in the right place

localStorage for user preferences, sessionStorage for transient state, IndexedDB for structured data.

Use Cases

When to histogram

EDA, perf data, AB tests, science.

Latency analysis

Spot tail latencies in request times. Random values come from crypto.getRandomValues — cryptographically secure for tokens, salts, and session IDs. Math.random is deterministic enough for animations but predictable enough to be a security bug.

CSPRNG-backed

AB test results

Compare arms by metric distribution. Pair with hashids, ULID, or UUID v7 when you need both random and sortable — useful for primary keys, public-facing IDs, and event log ordering without a sequence column.

reproducible

Exploratory analysis

First look at any numeric dataset. Output is deterministic given the same seed, so fixtures and tests stay reproducible across CI runs and contributor machines. Embed the seed in the fixture file for full replay.

snappy

Quality control

Detect drift in production measurements. Heavy work runs through useMemo so the UI stays snappy on large inputs. Same input + same options yields identical bytes — the kind of stability snapshot tests rely on.

feature-detected

Survey responses

Show response distributions per question. Storage tools persist to the right backend: localStorage for user preferences, sessionStorage for transient state, IndexedDB for structured data. Pick the right shelf and quota worries disappear.

deterministic

Education

Teach distributions interactively. Browser feature detection: clipboard, fullscreen, gamepad, and Web Bluetooth APIs gracefully fall back when unavailable. No surprise white screens on Safari or older Firefox.

production-ready

FAQ

Frequently asked questions

How are bin edges chosen?

Equal-width bins between min and max. The last bin is closed on both sides; others closed-open.

What's a good bin count?

5-15 for small samples, √n or Sturges' rule for larger. The slider lets you experiment.

Are the bars proportional?

Yes — bar width is percent of max bin count, so the tallest bar fills the row.

Are non-numbers ignored?

Yes — anything that doesn't parse to a finite number is silently dropped.

Is anything sent to a server?

No. Binning runs entirely in your browser.

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