| Open Source Virtual Backgrounds With Python and OpenCV |
With so much of the world stuck at home thanks to the COVID-19 pandemic, more and more workplaces are using video conferencing solutions—such as Zoom—to bring work to your living room. Or kitchen. Or bedroom. Or bathroom. OK… hopefully not that last one! Why not spice up your video conferencing with some awesome, homemade virtual backgrounds built with Python and OpenCV?
Inheritance and Composition: A Python OOP Guide
Learn about inheritance and composition in Python. You’ll improve your object-oriented programming (OOP) skills by understanding how to use inheritance and composition and how to leverage them in their design.
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Scout APM for Python
Check out Scout’s developer-friendly application performance monitoring solution for Python. Scout continually tracks down N+1 database queries, sources of memory bloat, performance abnormalities, and more. Get back to coding with Scout →
Stop Naming Your Python Modules
“Do not use
utils as a name for your Python module neither put it into a class name. Try to be more specific about the role of code – e.g. create a place for validators, services or factories. If the role criterion doesn’t help and you really dealing with utils, try grouping code by its theme.”
SEBASTIAN BUCZYŃSKI opinion
From Chunking to Parallelism: Faster Pandas With Dask
In a recent article, Itamar Turner-Trauring discussed how to read large datasets with Pandas using a chunking technique to reduce memory overhead. Chunking has another advantage: it enables parallelism, which can dramatically speed up processing time. Learn why and how to enable parallelism for your Pandas processing code using the Dask library.
Data Science: Reality Doesn’t Meet Expectations
“Seven common ways a data science role may not meet your expectations through tens of data scientist interviews and anecdotes from popular media.” Also see the related discussion on Hacker News
DAN FRIEDMAN opinion
How to Provide Test Fixtures for Django Models in
In this step-by-step tutorial, you’ll learn how to use fixtures to simplify your testing in pytest. If you use Django, pytest fixtures can help you write tests that are painless to update and maintain even as you make changes to your models.
PyPy 7.3.1 Released
Can You Retrieve Elements Silently Consumed by
zip() From Generators of Unequal Length?
If you pass two generators to
zip(), then the iterator returned by
zip() stops whenever the shorter of the two generators runs out of values. If the generator passed to
zip() in the first position is larger than the second, then there is an “extra” value consumed from the larger generator. Why does this happen, and is it possible to retrieve the extra value consumed by
How Can I Simplify Repetitive
Comparing a single value against a number of conditions can result in long
elif blocks. Is there a short and clean way to handle these comparisons?
pytest Maintainers Leaving
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Python Data Engineer (Remote)
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Articles & Tutorials
CuPy Accelerates NumPy on the GPU? Hold My Cider, Here’s Clojure!
NumPy is fast, but it doesn’t play well with GPUs. For that, you can use CuPy, a drop-in NumPy replacement for GPUs developed by Nvidia. Now, what if I told you to crunch those numbers in Clojure on a GPU instead? Surely a language that compiles to Java couldn’t beat C++ optimized for CUDA, could it? Dragan Djuric, author of Numerical Linear Algebra for Programmers compares CuPy and Clojure on a GPU, with some surprising results.
3 Ways to Test S3 in Python
Testing an application’s interaction with a third-party service can be difficult and frustrating. But sometimes a lot of business logic is wrapped up in those interactions, so testing them is important for building trust in the application. This article compares and contrasts three options for testing Amazon S3 integration using the pytest framework.
Neural Network Showdown: TensorFlow Vs PyTorch
TensorFlow and PyTorch are the two Python libraries that have accelerated the use of neural networks. This post goes over their pros and cons, so you can decide which to use in your next data science project. Check out ActiveState’s blog →
Quick Domain-Specific Languages in Python With
Domain-specific languages (DSL) bring the power of high-level programming to domain experts (read: non-programmers). In this article, Federico Tomassetti walks you through the process of creating a DSL with the textX Python library. As a bonus, you’ll also learn how to create syntax highlighting extension for VS Code that understands your language.
Visualizing Decision Trees With Python
“Decision trees are a popular supervised learning method for a variety of reasons. Benefits of decision trees include that they can be used for both regression and classification, they don’t require feature scaling, and they are relatively easy to interpret as you can visualize decision trees. This is not only a powerful way to understand your model, but also to communicate how your model works. Consequently, it would help to know how to make a visualization based on your model.”
MICHAEL GALARNYK • Shared by Michael Galarnyk
John Conway, Inventor of the Game of Life, Has Died of COVID-19
John Horton Conway was one of the most influential mathematicians of the 20th and 21st centuries. His “Game of Life” spawned countless implementation—nearly becoming a rite of passage for programmers in any language. Sadly, the world lost this brilliant mind on April 11 due to complications from COVID-19. Renowned mathematician Terry Tao has also published a memoir in honor of John Conway on his blog.
Modeling Stock Portfolios With Python
After reading an article about comparing a stock portfolio to the S&P 500, Matt Grierson wanted to go deeper and model a portfolio’s performance by analyzing buys and sells from a CSV file and calculating the rate of return relative to an index across any specified timeframe. This turned out to be more involved than Matt first thought it would be! Fortunately, Matt decided to share his experience—and his solution.
Adventures in Manipulating Python ASTs
George Ho proposed a change to PyMC4’s model specification API that he thought would help make the user experience more straightforward. Although those changes weren’t ultimately accepted by his fellow PyMC4 core developers, the process led him into some interesting explorations into Python’s abstract syntax tree.
RPP Episode #4: Learning Python Through Errors
An interview with Martin Breuss on getting started with Django, and how to learn Python through errors, and how errors really are your friends. Martin talks about his work with Coding Nomads, and teaching Python around the world. He also provides some tips on debugging and writing good questions.
REAL PYTHON podcast
Learn the Foundational Coding and Statistics Skills Needed to Start Your Career in Data Science
Interested in data science but not sure where to get started? Springboard’s Data Science Prep course was carefully crafted for go-getters ready for a challenge and need to brush up on a few basics before diving in to a data science bootcamp.
Combining Data in Pandas
In this step-by-step tutorial, you’ll learn three techniques for combining data in Pandas:
concat. Combining Series and DataFrame objects in Pandas is a powerful way to gain new insights into your data.
An Overview of Profiling Tools for Python
Gaining Insights Into Your Python Code Using the
Projects & Code
deltapy: Tabular Data Augmentation
twint: An Advanced Twitter Scraping & OSINT Tool
Whole-Foods-Delivery-Slot: Automation for Whole Foods and Amazon Fresh Deliveries
textX: Domain-Specific Languages and Parsers Made Easy
yfinance: Yahoo Finance Market Data Downloader
chord: Interactive Chord Diagrams in Python
pymc4: High-Level Probabilistic Programming Interface for TensorFlow Probability
machine_learning_examples: Collection of Machine Learning Examples and Tutorials
nimporter: Compile Nim Extensions for Python on Import!
PePy.tech: Download Stats for Python Packages
PEPY.TECH • Shared by Petru Rares Sincraian
Contextualise: Manage Your Knowledge
GITHUB.COM/BRETTKROMKAMP • Shared by Brett Kromkamp