Histogram

When to use

Histograms are useful for:

Understanding the shape and spread of a dataset (normal, skewed, bimodal…).
Identifying outliers and gaps in data.
Comparing distributions before and after a transformation.

Use ax.Histogram(values, chart.HistogramStyle{...}).

Basic example

API response latency measured across 120 production requests — a classic right-skewed distribution with a long tail of slow outliers.

package main

import (
	"github.com/goplotlib/goplotlib/chart"
	"github.com/goplotlib/goplotlib/plot"
)

func main() {
	// API endpoint response times (ms) — 120 production samples
	latency := []float64{
		18, 22, 19, 31, 25, 28, 23, 34, 27, 21,
		29, 41, 24, 33, 26, 39, 30, 45, 22, 37,
		32, 48, 27, 56, 35, 43, 29, 38, 52, 24,
		47, 36, 61, 28, 44, 33, 57, 31, 42, 25,
		68, 39, 29, 50, 36, 23, 45, 74, 31, 41,
		26, 53, 38, 30, 47, 34, 63, 28, 43, 37,
		82, 32, 49, 27, 55, 40, 35, 66, 29, 46,
		24, 58, 33, 44, 71, 30, 51, 38, 27, 48,
		92, 36, 43, 25, 62, 34, 47, 29, 55, 39,
		28, 75, 32, 46, 24, 59, 37, 43, 31, 52,
		21, 84, 35, 48, 26, 67, 33, 44, 28, 57,
		123, 38, 29, 53, 36, 42, 31, 46, 24, 68,
	}

	fig := plot.New(plot.WithWidth(860), plot.WithHeight(420))
	ax := fig.AddAxes()
	ax.Histogram(latency, chart.HistogramStyle{Label: "p50 endpoint", Bins: 20})
	ax.SetTitle("API Response Latency Distribution").
		SetXLabel("Latency (ms)").
		SetYLabel("Request count")
}

Histogram

Bin control

By default the number of bins is chosen automatically via Sturges’ rule (k = ⌈log₂(n) + 1⌉). You can override this:

// Fixed number of bins
ax.Histogram(latency, chart.HistogramStyle{Bins: 30})

// Explicit bin edges — useful for SLA buckets
ax.Histogram(latency, chart.HistogramStyle{
	BinEdges: []float64{0, 25, 50, 100, 200, 500},
})

Density and cumulative modes

// Normalise so the total area equals 1 (probability density)
ax.Histogram(latency, chart.HistogramStyle{Normalize: true})

// Cumulative count / density
ax.Histogram(latency, chart.HistogramStyle{Cumulative: true})

// Both together — cumulative density function (CDF)
ax.Histogram(latency, chart.HistogramStyle{Normalize: true, Cumulative: true})

Style reference

chart.HistogramStyle fields:

Field	Type	Default	Description
`Label`	`string`	`""`	Series label shown in the legend
`Color`	`color.Color`	palette	Override the automatic palette color
`Opacity`	`float64`	`1.0`	Overall bar opacity, 0.0–1.0
`Bins`	`int`	auto	Number of equal-width bins (0 = Sturges’ rule)
`BinEdges`	`[]float64`	—	Explicit bin edges (overrides `Bins`)
`Normalize`	`bool`	`false`	Normalise to density (area = 1)
`Cumulative`	`bool`	`false`	Show cumulative count or density

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