Creating Dashboards with Plotly

Dashboard idea

A “dashboard” can be as simple as:

• a few key charts
• shown together
• with a consistent theme

You don’t need a full web framework to get this far — Plotly supports multi-plot layouts via make_subplots, letting you assemble several independent figures into one shareable view.

How the pieces fit together

Building a Plotly dashboard is really a small pipeline: each data source feeds one chart, and every chart’s traces get placed into a shared grid figure.

diagram From data sources to a mini dashboard mermaid

  flowchart LR
  A["sales DataFrame"] --> B["px.bar(...)
Sales by city"]
  C["trend DataFrame"] --> D["px.line(...)
Orders trend"]
  B --> E["make_subplots(rows, cols)"]
  D --> E
  E --> F["fig.add_trace(...) per chart"]
  F --> G["One figure, one layout,
fig.show() renders the dashboard"]

Each dataset becomes one chart; make_subplots collects every chart's traces into a single figure and layout.

Example: 2 charts in one view

Subplots dashboard

import pandas as pd
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
 
sales = pd.DataFrame({
    "city": ["Pune", "Delhi", "Mumbai", "Bengaluru"],
    "sales": [120, 180, 90, 160],
})
 
trend = pd.DataFrame({
    "day": [1, 2, 3, 4, 5],
    "orders": [120, 140, 130, 160, 155],
})
 
bar = px.bar(sales, x="city", y="sales", title="Sales by city")
line = px.line(trend, x="day", y="orders", markers=True, title="Orders trend")
 
fig = make_subplots(rows=1, cols=2, subplot_titles=("Sales", "Orders"))
 
for trace in bar.data:
    fig.add_trace(trace, row=1, col=1)
for trace in line.data:
    fig.add_trace(trace, row=1, col=2)
 
fig.update_layout(title_text="Mini dashboard", showlegend=False)
fig.show()

Note the pattern: build each chart normally with Plotly Express, then copy its .data traces into a shared make_subplots grid. You get one figure — one HTML export, one set of hover/zoom controls — instead of juggling several separate files.

Keeping it readable

• Give every subplot its own clear subplot_titles entry — a dashboard with unlabeled panels forces the reader to guess.
• Keep a consistent color scheme across panels so the same category means the same color everywhere.
• Don’t cram more than 4-6 charts into one static grid; beyond that, a real dashboarding tool (below) manages layout and filtering far better.

Analyst’s tip

The moment two charts on a dashboard should respond to each other — like clicking a bar to filter the line chart next to it — you’ve outgrown make_subplots. That kind of cross-chart interactivity needs an app with a running Python backend (Dash) or a no-code layer (Streamlit), not just a static HTML export.

Next step

If you want a full web app dashboard with filters, dropdowns, and cross-chart interactivity, the next tool is usually Dash or Streamlit — both build on top of the same Plotly figures you already know how to create.

Next

Continue to: Choropleth Maps for visualizing metrics across geographic regions.

🧪 Try It Yourself

Exercise 1 – Create a Subplot Grid

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Exercise 2 – Add a Trace to a Specific Cell

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Exercise 3 – Label Each Panel

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