Plotly vs. Matplotlib for Dashboards (and What Happens After You Make the Chart)

Ask a room of analysts whether to use matplotlib or Plotly and you'll get a strong opinion from everyone and a consistent answer from no one. That's because the real decision usually isn't about the library — it's about what happens five minutes after the chart renders.

The actual difference between them

Matplotlib is static by default, has been stable for over two decades, and produces publication-quality output with fine-grained control. Plotly is interactive by default — hover states, zoom, pan — and renders to HTML/JS instead of a raster or vector image.

# matplotlib — static, precise, fast to render
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(df["quarter"], df["revenue"])
ax.set_title("Quarterly Revenue")
plt.savefig("revenue.png", dpi=200)

# plotly — interactive, browser-native
import plotly.express as px

fig = px.line(df, x="quarter", y="revenue", title="Quarterly Revenue")
fig.write_html("revenue.html")

Both are correct code. Both produce a reasonable chart. Neither line tells you what to actually do with the output — and that's where the real fork in the road is.

The question that actually matters

Not "static or interactive," but: who's going to look at this, and how?

• If it's going in a printed report or a PDF that needs to look identical everywhere → matplotlib's static output is the right call, and Plotly's interactivity is wasted.
• If someone needs to explore the data themselves — zoom into a date range, hover for exact values → Plotly's interactivity earns its keep.
• If it needs to go on a live, shareable page that updates when the underlying data updates → neither library actually solves this on its own. fig.write_html() gives you a static snapshot of an interactive chart, not a live one.

That last point trips people up constantly. A Plotly chart feels alive because you can interact with it locally — but the moment you export it, it's frozen at the data it had when you ran the script. Sending someone that HTML file is functionally the same as sending them a screenshot: accurate right up until the data changes.

Closing the actual gap

The distinction that matters isn't matplotlib vs. Plotly — it's exported snapshot vs. live query. A chart that re-runs its query every time someone opens it behaves completely differently from one that's frozen at export time, regardless of which library drew it.

# a Pyodide node in Infigured — same plotly code,
# but backed by a query object instead of a static dataframe
import plotly.express as px

df = infig.query("quarterly_revenue_by_region")
fig = px.line(df, x="quarter", y="revenue", color="region")
fig

The chart above renders the same way whether it's on a canvas you're actively editing, a presentation you're stepping through, or a published page someone else opens next week — and because it's backed by a query object rather than a frozen dataframe, it can be set to re-run rather than stay static.

The practical takeaway

Pick matplotlib or Plotly based on whether you need interactivity for exploration — that's a real, valid decision. But treat "how does this get shared, and does it go stale" as a separate decision entirely, because neither library answers it for you.

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Related: How to Present KPIs So People Actually Read Them