InfoQ Homepage Python Content on InfoQ
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Probabilistic Programming from Scratch
Mike Lee Williams demonstrates building a lightweight probabilistic programming system from scratch with Python. He also explores how solving problems using PyMC3.
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Understanding Python Memory at Instagram
Min Ni discusses how Python memory profiling is done at Instagram, insights from memory profiling data, and learnings from tuning and improving Python memory garbage collection.
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A Practical Road to SaaS in Python
Armin Ronacher discusses his experiences building SaaS businesses on a Python technology stack from a security and scalability point of view, and what other technologies work well with Python.
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Building Data Pipelines in Python
Marco Bonzanini discusses the process of building data pipelines and all the steps necessary to prepare data, focusing on data plumbing and going from prototype to production.
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Machine Learning and End-to-End Data Analysis Processes in Spark Using Python and R
Debraj GuhaThakurta discusses ML and data analysis processes in Spark using examples written in Python and R.
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The Joy of Not Coding
Jeroen Janssens discusses several tricks for polyglot programmers helping to mix and match different languages and tools in a project.
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Scripting Eclipse with Python
Tracy Miranda demonstrates Python with the Eclipse Advanced Scripting Environment (EASE) for collaboration, reproducible research, and exploratory computation and data analysis.
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Hunting Criminals with Hybrid Analytics
David Talby demos using Python libraries to build a ML model for fraud detection, scaling it up to billions of events using Spark, and what it took to make the system perform and ready for production.
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Culture and the Games People Play
Roy Rapoport discusses the power of alignment (or lack thereof) using real-world examples, his experience introducing Python in production, and the organizational structures and culture within Netflix
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Spring XD Today and Tomorrow
Mark Pollack discusses Spring XD and its integration driven by the Big Data ecosystem at large such as Kafka, Spark, functional programming, integration with Python, and designer/monitoring UIs.
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Python: Why Are the Big Dealers Making Big Bets?
The authors demonstrate the design and use of an environment for quantitative researchers building a market risk simulation first as a basic system and then adding a hypothetical systemic shock.
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A Guide to Python Frameworks for Hadoop
Uri Laserson reviews the different available Python frameworks for Hadoop, including a comparison of performance, ease of use/installation, differences in implementation, and other features.
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