Heat Map Creator Online

Turn matrix data into a visual heat map. Use color gradients to highlight patterns, correlations, and densities in your data.

Heat Map Data

Enter data as comma-separated values (row, column, value), one per line

Chart Title

Color Scale

Cell Padding: 2px

Corner Radius: 2px

Show Values

Show Labels

Color Scale Preview

318

Heat Map Preview

Activity Heat Map

9am

10am

11am

12pm

1pm

2pm

3pm

Mon

Tue

Wed

Statistics

Data Points

Total

184

Mean

8.76

Median

8.00

Min

Max

Data Summary

Row	Column	Value
Mon	9am	5
Mon	10am	8
Mon	11am	12
Mon	12pm	15
Mon	1pm	10
Mon	2pm	7
Mon	3pm	4
Tue	9am	6
Tue	10am	9
Tue	11am	14
Tue	12pm	18
Tue	1pm	11
Tue	2pm	8
Tue	3pm	5
Wed	9am	4
Wed	10am	7
Wed	11am	10
Wed	12pm	13
Wed	1pm	9
Wed	2pm	6
Wed	3pm	3

How it works

Enter your data as a matrix with row labels, column labels, and values. Each cell becomes a colored square, with color intensity representing the value. Higher values get more intense colors.

Choose a color scheme: sequential (light to dark) for magnitude, diverging (two colors) for positive/negative values, or categorical for distinct groups. The color scale automatically adjusts to your data range.

Data format:

Day, 9AM, 10AM, 11AM, 12PM Monday, 45, 78, 92, 65 Tuesday, 52, 81, 88, 71 Wednesday, 48, 75, 95, 68

Interactive tooltips show exact values on hover. Patterns emerge instantly - high and low values cluster visibly. Export for reports showing patterns across two dimensions.

When You'd Actually Use This

Website click tracking

Show which page areas get most clicks. Designers see hot and cold zones. UX improvements target low-engagement areas.

Sales by day and hour

Identify peak sales times. Staff scheduling matches demand patterns. Revenue increases with optimal staffing.

Correlation matrix visualization

Show correlations between many variables. Strong correlations stand out. Data scientists identify relationships quickly.

Gene expression analysis

Biologists visualize gene activity across conditions. Patterns reveal gene clusters and relationships. Research insights emerge visually.

Performance metrics dashboard

Show KPIs across departments and time. Red cells flag problems instantly. Management attention goes where needed.

Calendar activity visualization

GitHub-style contribution graphs show activity over time. Streaks and gaps are obvious. Motivation comes from visual consistency.

What to Know Before Using

Color choice affects interpretation.Sequential schemes show magnitude. Diverging schemes show deviation from center. Choose based on your data story.

Colorblind accessibility matters.Red-green colorblindness is common. Use colorblind-safe palettes. Add patterns or labels for critical distinctions.

Cell size affects pattern visibility.Too small: patterns blur together. Too large: subtle variations hide. Adjust cell size for your data density.

Outliers can compress the scale.One extreme value makes all others look similar. Consider log scale or capping extremes for better differentiation.

Pro tip: Add row and column clustering to group similar patterns. Related rows/columns appear adjacent, revealing hidden structures.

Common Questions

How many cells work best?

10x10 to 20x20 works well. Smaller matrices may be better as tables. Larger matrices become hard to read individually.

What color scheme should I use?

Sequential (light blue to dark blue) for magnitude. Diverging (blue-white-red) for positive/negative. Categorical for distinct groups.

Can I show missing data?

Yes, use gray or white for missing values. This distinguishes missing from zero. Important for accurate interpretation.

Should I add value labels?

For small matrices, yes. For large ones, labels create clutter. Use tooltips for exact values, colors for patterns.

How do I handle negative values?

Use a diverging color scheme with neutral center. Negative values one color, positive another. Zero or mean at center.

Can I reorder rows and columns?

Yes, clustering or manual reordering reveals patterns. Group similar rows/columns together. Order affects pattern visibility.

What's the difference from choropleth maps?

Heat maps show data matrices. Choropleth maps show geographic data. Same visual encoding, different data types.

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