When to reach for a scatter plot

A scatter plot answers “how do these two numeric variables relate?” Each point is one row of data, positioned by two measurements at once. Add colorcolor for a third variable (a category) and sizesize for a fourth (a number) — a scatter plot can honestly encode more information per point than almost any other chart type.

Basic scatter

import pandas as pd
import plotly.express as px
 
df = pd.DataFrame({
    "hours": [1, 2, 3, 4, 5, 6, 7, 2, 4, 6],
    "score": [50, 55, 65, 70, 75, 78, 85, 52, 69, 77],
    "group": ["A", "A", "A", "A", "A", "A", "A", "B", "B", "B"],
})
 
fig = px.scatter(df, x="hours", y="score", color="group", title="Hours vs score")
fig.show()

import pandas as pd
import plotly.express as px
 
df = pd.DataFrame({
    "hours": [1, 2, 3, 4, 5, 6, 7, 2, 4, 6],
    "score": [50, 55, 65, 70, 75, 78, 85, 52, 69, 77],
    "group": ["A", "A", "A", "A", "A", "A", "A", "B", "B", "B"],
})
 
fig = px.scatter(df, x="hours", y="score", color="group", title="Hours vs score")
fig.show()

Every point is hoverable by default — Plotly shows xx, yy, and colorcolor for whichever point the cursor is nearest, with zero extra code.

Add trend line

import plotly.express as px
 
fig = px.scatter(df, x="hours", y="score", color="group", trendline="ols", title="Trend line")
fig.show()

import plotly.express as px
 
fig = px.scatter(df, x="hours", y="score", color="group", trendline="ols", title="Trend line")
fig.show()

trendline="ols"trendline="ols" fits an ordinary-least-squares regression line per color group and draws it on top — a quick way to see if a relationship looks roughly linear (requires statsmodelsstatsmodels installed).

Tips

• Use hover_data=[...]hover_data=[...] to show more context (e.g. a name or ID column) in the tooltip.
• Trend lines can be sensitive to outliers — a single extreme point can tilt the whole fitted line.
• If points overlap heavily, lower opacityopacity or switch to a 2D density/contour plot.

  flowchart LR
  A["One data row"] --> B["x position
(variable 1)"]
  A --> C["y position
(variable 2)"]
  A --> D["color
(category, optional)"]
  A --> E["size
(magnitude, optional)"]
  B & C & D & E --> F["One point,
hoverable for exact values"]

Next

Continue to: Bubble Charts to add a size dimension to your scatter plots.

🧪 Try It Yourself

Exercise 1 – Plot Two Variables

Exercise 2 – Color by Group

Exercise 3 – Add a Trend Line