Interactive Histograms and Distributions

Why interactive distributions

A histogram answers “how are these values spread out?” by grouping numbers into bins and counting how many fall in each. Interactive distributions help you:

• zoom into interesting ranges (drag across a busy region of bars)
• inspect outliers (hover a thin bar far from the rest)
• compare groups with hover tooltips (see the exact count per bin, per category)

Histogram

Histogram

import pandas as pd
import plotly.express as px
 
df = px.data.tips()
 
fig = px.histogram(
    df,
    x="total_bill",
    nbins=30,
    title="Total bill distribution",
)
fig.show()

nbins controls how many bins the range is split into — too few bins hide structure (everything looks like one lump), too many bins make the shape noisy (every bar is its own spike).

Histogram by category

Histogram by sex

import plotly.express as px
 
df = px.data.tips()
 
fig = px.histogram(
    df,
    x="total_bill",
    color="sex",
    nbins=25,
    barmode="overlay",
    opacity=0.6,
    title="Total bill distribution by sex",
)
fig.show()

barmode="overlay" stacks both distributions on top of each other (instead of side by side); lowering opacity lets you see where the two overlap.

sketch A histogram is a bar chart of counts per bin p5.js Pause Restart

Raw values are grouped into equal-width bins; bar height = how many values landed in that bin.

Box plot (interactive)

Interactive box plot

import plotly.express as px
 
df = px.data.tips()
 
fig = px.box(df, x="day", y="total_bill", color="sex", title="Total bill by day")
fig.show()

A box plot summarizes a distribution’s median, quartiles, and outliers in one compact shape — useful when you’re comparing many groups and a full histogram per group would be too much to look at.

Tip

Start with a histogram, then add:

• category color
• hover fields
• facet columns/rows

Analyst’s tip

Bin count is not a neutral choice — the same data can look unimodal or bimodal depending purely on nbins. Try a few bin counts before drawing conclusions about the shape of a distribution, and don’t cherry-pick the one that best fits a story you already believe.

diagram From raw values to a histogram mermaid

  flowchart LR
  A["Raw numeric column"] --> B["Split range into nbins
equal-width buckets"]
  B --> C["Count values
falling in each bucket"]
  C --> D["Draw one bar per bucket
height = count"]

Values are grouped into equal-width bins, then each bin's count becomes a bar.

Next

Continue to: Plotly Subplots and Facets for comparing many distributions side by side.

🧪 Try It Yourself

Exercise 1 – Build a Histogram

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Exercise 2 – Overlay Two Groups

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Exercise 3 – Summarize with a Box Plot

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