Introduction Static charts have served data analysts well for decades, but in today’s collaborative and presentation-driven environment, interactive visualizations are becoming the norm. When you share a matplotlib PNG on Slack or embed a seaborn chart in a report, your audience sees a frozen snapshot. But what if they could hover to see exact values, filter data on the fly, zoom into specific regions, or toggle between different views? This is where Plotly and Streamlit shine. Plotly transforms your Python data into rich, interactive HTML visualizations that work in notebooks, dashboards, and web apps. Streamlit wraps these visualizations in a clean, shareable web interface with minimal code. Together, they enable you to build data products that tell stories, invite exploration, and go viral on platforms like Twitter and LinkedIn. In this first episode of our data storytelling series, we’ll transition from static charts to interactive dashboards. We’ll cover Plotly Express for rapid prototyping, Plotly Graph Objects for fine-grained control, and Streamlit for building full-featured dashboards. We’ll use the World Happiness Report dataset from Kaggle to
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