Python Visualization

Good charts turn numbers into decisions. These 5 tutorials cover Matplotlib from basic plots to subplots and annotations, Seaborn for statistical charts and heatmaps, and Plotly for interactive dashboards. A head-to-head comparison post helps you pick the right library for each job.

Streamlit Tutorial: Turn a Python Script into a Data App

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Ten lines of plain Python is all it takes to turn a data script into a web app your teammate can open in a browser. That is what this Streamlit tutorial shows you: no HTML, no JavaScript, no front-end framework. You write Python, add a few Streamlit calls, and it draws the buttons, sliders, charts, and tables. By the end you will have a data explorer with upload, filters, a live chart, and a metrics row, and know how to ... Read More

Plotly: Interactive Visualizations & Dashboards

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You have a chart, and the first question in every meeting is the same: “What is the exact value at that spike?” A static PNG cannot answer. Python Plotly can: hover for the number, drag to zoom, click a legend entry to hide a series. This post covers building interactive charts, saving them as one shareable HTML file, and picking the right Plotly API for the job. “Above all else, show the data.” Edward Tufte, The Visual Display of Quantitative ... Read More

Python: Matplotlib vs Seaborn vs Plotly, Visualization Libraries Compared

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Python visualization really comes down to three big names: Matplotlib, Seaborn, and Plotly. This guide draws the same chart in all three libraries, lines up their strengths and weaknesses, looks at the speed difference, and hands you a simple decision tree so you stop second-guessing which one to reach for. “The greatest value of a picture is when it forces us to notice what we never expected to see.” John Tukey, Exploratory Data Analysis Last Updated: July 2026 | Tested ... Read More