Tag: Channel Specials
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Using matplotlib/plotly for infographic-style outputs
Infographic-style data visualization is reshaping how engineers communicate insights. This guide explores modern techniques using Matplotlib and Plotly to create polished, data-rich visuals that blend scientific accuracy with design precision. Learn when to use each tool, best practices for layout and color, and how to integrate both in automated workflows.
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Empirical: float precision errors in Python math
This empirical exploration dives into how Python handles floating-point precision, why tiny rounding errors appear in mathematical computations, and how engineers can empirically benchmark and mitigate them. We explore IEEE 754 internals, Python precision libraries, and real-world benchmarking strategies for robust numerical computation.
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Introduction to benchmarking in Python
Benchmarking is one of the most valuable skills for Python developers aiming to write efficient and scalable code. This post introduces the fundamentals of benchmarking in Python, from basic timing techniques to powerful libraries like timeit and pytest-benchmark.
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Introduction to infographics and visual learning for Python/ML
Visual learning is transforming how Python and machine learning practitioners communicate data and concepts. This post introduces infographics as a tool for better comprehension, exploring key libraries, design principles, and modern visualization workflows that enhance understanding and storytelling in data science.
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Using FastAPI to build a mini REST API in Python
FastAPI has rapidly become one of the go-to frameworks for building high-performance REST APIs in Python. In this guide, we will walk through creating a mini REST API from scratch using FastAPI, explore how it compares with Flask and Django REST Framework, and learn practical tips for testing, documentation, and deployment.
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Using timeit and perf to benchmark Python code
This guide explores the practical use of Python’s built-in timeit module and the powerful perf library for accurate benchmarking. Learn how to perform reproducible, statistically robust performance testing using both tools, interpret their results, and integrate them into modern engineering workflows.
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Best practices: avoid mutable default arguments
Mutable default arguments in Python can lead to unpredictable bugs because they are evaluated once at function definition, not at each call. This post explains why this happens, demonstrates real-world implications, and provides modern best practices, tools, and patterns to avoid these issues safely.