Heat Map Examples: 7 Types of Heat Maps with Real Use Cases
The seven most common heat map examples are the correlation matrix, the calendar heatmap, the hour × weekday traffic grid, the geographic choropleth, the website click map, the cohort retention grid, and the risk matrix. Each encodes a number as color intensity on a grid — the difference is what the rows and columns represent.
Quick answer: Match the job to the type: correlation matrix → relationships between variables; calendar heatmap → daily activity over a year; hour × weekday grid → traffic and staffing patterns; choropleth → values by geographic region; click map → where users click or scroll on a page; cohort retention grid → retention by signup cohort; risk matrix → likelihood vs. impact for a risk register. All seven map a number to color; only the grid axes differ.
If you need the fundamentals first — what the color scale encodes and when a heat map beats a table — start with heat maps explained. Below are the seven types you'll actually build, each with what it shows, when to use it, and the fastest tool for the job.
What Does a Correlation Matrix Heat Map Show?
A correlation matrix heat map shows the correlation coefficient between every pair of numeric variables in a dataset, colored from -1 (strong negative, usually blue) through 0 (white) to +1 (strong positive, usually red). Clusters of dark cells reveal groups of variables that move together.
When to use it: the first ten minutes of exploratory data analysis, feature selection before modeling, or checking for multicollinearity in a regression.
Tool: Python's seaborn does this in two lines; numeric_only=True skips text columns, which otherwise make df.corr() raise an error in pandas 2.0 and later. See the full seaborn heatmap tutorial for the mask trick that hides the redundant upper triangle:
import seaborn as sns
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap="coolwarm", center=0)When Should You Use a Calendar Heat Map?
Use a calendar heat map when you have daily counts spanning months and care about streaks, gaps, and weekly rhythm ("why are Sundays always empty?"). A line chart of the same data hides the weekday pattern; the calendar layout exposes it.
What it shows: one colored square per day, arranged in a week-by-week grid across a year. Darker squares mean more of something — commits, sales, workouts, support tickets. GitHub's contribution graph made this layout famous. The grid surfaces daily seasonality at a glance; for decomposing that seasonality from the underlying trend, see time series analysis.
Tool: calplot or july in Python, ECharts' calendar coordinate system in JavaScript, or a clever Excel grid (row per weekday, column per week) with conditional formatting.
How Do You Visualize Traffic by Hour and Day of Week?
Use a time-of-day × weekday heat map: a 7 × 24 grid with weekdays down the side, hours across the top, and color showing volume, whether that's website sessions, call-center load, gym check-ins, or emergency room arrivals. The "hot" blocks jump out instantly.
When to use it: staffing and scheduling decisions. This is the single most actionable heat map in business analytics because the answer ("Tuesday 10am–1pm is peak") is directly operational.
Tool: a pivot table (weekday rows, hour columns, count values) plus a color scale — in pandas + seaborn, Excel, or any BI tool. Build one step-by-step in how to make a heatmap.
pivot = df.pivot_table(index="weekday", columns="hour",
values="sessions", aggfunc="sum")
sns.heatmap(pivot, cmap="YlOrRd")What Is a Geographic Heat Map (Choropleth)?
A choropleth is a map whose regions — countries, states, zip codes — are shaded by a value such as population density, sales per region, election margins, or COVID case rates.
When to use it: whenever "where?" is the question. One caution: large, sparsely populated regions dominate visually, so prefer rates (sales per capita) over raw totals, or the map just shows you where people live.
Tool: Plotly's px.choropleth, Folium, Tableau, or Power BI's built-in map visuals. For point data (crime incidents, delivery drops) use a density heat map layer instead of region shading.
What Does a Website Click Heat Map Show?
A website click (or attention) heat map overlays a screenshot of a page with color showing where users clicked, moved the mouse, or how far they scrolled. Red blobs mean heavy interaction; cold areas mean ignored content.
When to use it: UX and conversion optimization — finding buttons nobody clicks, non-clickable elements everyone clicks, and the scroll depth where readers give up.
Tool: this one you buy rather than build: Hotjar, Microsoft Clarity (free), or Crazy Egg record real user sessions and render the overlay for you.
What Is a Cohort Retention Heat Map?
A cohort retention heat map is a grid where rows are signup cohorts (e.g., "January signups"), columns are periods since signup (month 0, 1, 2, …), and each cell is the percentage of that cohort still active. Color makes the retention cliff — and any improvement in newer cohorts — visible at a glance.
When to use it: subscription and product analytics. Reading down a column compares cohorts at the same age; a triangle that gets darker toward the bottom rows means retention is improving over time.
Tool: pandas pivot_table + seaborn, or built-in cohort reports in Amplitude and Mixpanel.
retention = df.pivot_table(index="cohort_month",
columns="months_since_signup",
values="pct_retained")
sns.heatmap(retention, annot=True, fmt=".0%", cmap="Greens")What Is a Risk Matrix Heat Map?
A risk matrix heat map is a small grid — typically 5 × 5 — with likelihood on one axis, impact on the other, and cells colored green → yellow → red. Individual risks are plotted into cells so stakeholders see what needs attention first.
When to use it: project risk registers, security assessments, compliance reviews. Its power is communication, not precision: the categories are judgment calls, but the red corner focuses a meeting instantly.
Tool: Excel or PowerPoint is genuinely fine here — a 5 × 5 range with fixed cell colors and labels typed in. No library needed.
Which Heat Map Example Should You Use for Your Data?
Match the grid axes to your question. Variables × variables → correlation matrix. Days across a year → calendar. Hours × weekdays → traffic grid. Regions → choropleth. Page coordinates → click map. Cohorts × age → retention grid. Likelihood × impact → risk matrix. If your data doesn't naturally form a grid, a heat map is probably the wrong chart — reach for a bar or line chart instead.
| Question | Heat map type | Fastest tool |
|---|---|---|
| Which variables move together? | Correlation matrix | seaborn |
| What does daily activity look like over a year? | Calendar heatmap | calplot / ECharts |
| When are we busiest? | Hour × weekday grid | Pivot + color scale |
| Where is the value concentrated geographically? | Choropleth | Plotly / Tableau |
| Where do users click? | Click map | Clarity / Hotjar |
| Are newer customers sticking around longer? | Cohort retention grid | pandas + seaborn |
| Which risks matter most? | Risk matrix | Excel |
Pro Tip: Every heat map lives or dies by its color scale. Use a sequential scale (light → dark) for counts and magnitudes, and a diverging scale centered on a meaningful zero (like coolwarm at 0 correlation) only when values can be meaningfully "above" or "below" a midpoint. Mixing these up is the most common heat map mistake — and once the scale is right, resist adding heavy cell borders, 3D effects, or decorative gradients; see avoiding chartjunk for the full list of decoration to cut.