<ul><li>The article discusses creating heatmaps for time series data using Matplotlib in Python.</li><li>The data used in the article is related to measles cases from University of Pittsburgh’s Project Tycho.</li><li>The article demonstrates how to visualize measles incidence data over time using pcolormesh() function in Matplotlib.</li><li>Different heatmap functions like imshow() in Matplotlib are compared for creating visualizations.</li><li>The article highlights the importance of color selection in creating informative and visually appealing heatmaps.</li><li>It explains how color distribution in the heatmap can affect the interpretation of data.</li><li>The process of creating a custom colormap in Matplotlib to match a specific heatmap design is detailed.</li><li>The article discusses handling missing data and normalizing values for heatmap visualization.</li><li>Heatmaps are described as effective tools for analyzing trends, temporal patterns, and communicating complex data effectively.</li><li>They are valuable for comparative analysis, temporal trends, pattern recognition, and facilitating clear communication of data.</li></ul>

Heatmaps for Time Series

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