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14 Python scripts generated for climate stripes this week

Climate Stripes

Chart overview

Climate stripes represent each year's temperature anomaly relative to a baseline average as a single colored vertical stripe, using a blue-to-red diverging colormap popularized by climate scientist Ed Hawkins.

Key points

  • The design strips away all chart furniture to communicate the trend of global or regional warming with maximum visual impact and accessibility.
  • Scientists and science communicators use it to convey climate change evidence to broad audiences and in peer-reviewed publications.

Example Visualization

Sequence of vertical stripes transitioning from deep blue to deep red representing increasing annual temperature anomalies over decades

Create This Chart Now

Generate publication-ready climate stripess with AI in seconds. No coding required – just describe your data and let AI do the work.

View example prompt
Example AI Prompt

"Create a climate stripes visualization from my annual temperature anomaly data. Use a blue-white-red diverging colormap centered at zero anomaly, remove all axes and tick marks for a clean minimal design, add only a year range label at the bottom, and optionally overlay a smoothed trend line."

How to create this chart in 30 seconds

1

Upload Data

Drag & drop your Excel or CSV file. Plotivy securely processes it in your browser.

2

AI Generation

Our AI analyzes your data and generates the Climate Stripes code automatically.

3

Customize & Export

Tweak the design with natural language, then export as high-res PNG, SVG or PDF.

Python Code Example

Loading code...

Console Output

Output
Figure saved: plotivy-climate-stripes.png

Common Use Cases

  • 1Communicating regional warming trends to policy makers and the public
  • 2Visualizing multi-decadal sea surface temperature records from oceanographic datasets
  • 3Illustrating urban heat island intensification over the past century
  • 4Comparing warming rates across different climate zones in a multi-panel figure

Pro Tips

Center the diverging colormap at the 1961 to 1990 climatological mean, not at zero Celsius

Use RdBu_r reversed so red represents warming and blue represents cooling

Remove all spines, ticks, and labels for the canonical minimal design, keeping only years

Scale color limits symmetrically (e.g., plus or minus 2 degrees C) so the midpoint white truly represents the baseline

Free Cheat Sheet

Scientific Chart Selection Cheat Sheet

Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.

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