Line Charts

A line chart is the right tool when your data has a meaningful order along the x-axis — almost always time, but sometimes another continuous or ordered variable. Lines connect successive points to show how a value evolves.

When to use a line chart

Use a line chart when:

• The x-axis represents time, or another ordered, continuous variable.
• You want to show trend, change, or rate of change.
• You have multiple series to compare over the same axis (one line per group).

Never use a line chart when the x-axis is a set of unordered categories like "Apple, Banana, Cherry." Lines drawn between them imply a relationship that isn't there.

A simple time series

That's the simplest possible line chart: one x, one y, one line. Hover over the line — you'll see Plotly's tooltip pop up showing exact dates and values.

Multiple series with color

To show several lines, reshape your data to long form and use color to encode the series.

Now you have several lines, each in a different color, with an interactive legend you can click to hide/show series.

Markers: show the actual data points

Lines are great for showing the shape of a trend, but sometimes you want to also see exactly where each measurement was taken. Add markers=True:

The markers help when:

• You have sparse data (just a handful of points).
• The reader needs to know which are the measured points.
• The chart will be exported as a static PNG.

If you have dense data (thousands of points), omit markers — they'll just clutter the line.

Many lines? Avoid the spaghetti

A common failure mode is the spaghetti chart — twenty or more overlapping lines that no one can read.

There are 30 lines on that chart and even with interactive toggling it's hard to read. Fixes:

• Filter to a smaller set of series (top 5 countries by some metric).
• Use facets (small multiples) — one mini-chart per country.
• Highlight a few lines and gray out the rest.

We'll cover faceting in its own chapter. For now, the rule: if you have more than ~7 lines, plan how you'll keep them readable.

Linear and log scales

When values span many orders of magnitude (e.g., country GDPs, COVID case counts), a log scale for y reveals structure that a linear scale flattens out.

On the linear chart, the USA's huge values squash everyone else. On the log chart, you can compare the relative growth rates of all four countries.

Log scales are powerful but require labeling — always say "log scale" in your axis label, because viewers do not assume it.

A common subtle mistake

Plotly Express orders line points by their order in the DataFrame, not by the x-axis value. If your data isn't sorted by x, you'll get a tangled chart:

# Suspicious! Sort before plotting:
df = df.sort_values("date")
px.line(df, x="date", y="price")

If a line chart ever looks bizarrely scribbled, the first thing to check is whether the data is sorted by x.

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