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67 articles tagged #Python.
P.01The short answer: BullMQ for Node, Celery for Python, Sidekiq for Ruby, and Temporal or a cloud-managed queue when you need durability without owning broker infrastructure. Here's the reasoning, a comparison table, and working code.
P.02A background task that outlives the function that spawned it is a real, recurring production bug. Structured concurrency fixes it by tying a task's lifetime to a scope. Here's the pattern across Python, Kotlin, Swift, and Java.
P.03Google shipped an open-source Colab CLI that provisions T4, L4, A100, and H100 GPUs (and TPUs) from the command line. Here's how to install it, run your first job, and wire it into a terminal AI agent.
P.04100% code coverage tells you every line ran during your tests. It says nothing about whether your tests would notice if that line broke. Mutation testing answers that question directly, here's how to run it with Stryker and mutmut and what to do with the score.
P.05FastAPI merged a significant router internals refactor on June 14, 2026, followed by app.frontend() for serving SPAs a week later. Routes are now preserved instead of cloned, dynamic route registration works, and serving a React or Vue frontend from FastAPI no longer requires a workaround.
P.06Data scientist and ML engineer are two different jobs. This guide explains the distinction, what a strong data scientist actually does, and what to screen for in the interview to find someone with real analytical judgment.
P.07Everyone claims ML experience since the AI boom. This is the screen that tells apart someone who can build and ship ML systems from someone who fine-tuned a model in a Colab notebook once.
P.08Python is the language of AI, data, and backend. But 'Python developer' covers a huge range of skill sets. Here's how to define the role, screen for real ability, and avoid costly mismatches.
P.09Playwright has become the dominant web scraping tool, but the tooling decision is the easy part. Here's a practical guide to scraping that works, and the legal and ethical lines you need to know before shipping.
P.10async/await syntax makes Python code look concurrent. The behavior when you get it wrong is harder to predict than most tutorials explain. Here's what breaks in production and how to fix it before it does.
P.11Pydantic v2 shipped with a Rust-based core that made validation 5-50x faster. But the performance is only part of it — the v2 model redesign changes how you structure validation logic for complex APIs.
P.12Both frameworks can build RAG pipelines and agent systems, but they're designed with different priorities. Here's when to reach for each and when to skip both.
P.13dbt turns SQL SELECT statements into a tested, documented, version-controlled data pipeline. Here's how it works and when you should add it to your data stack.
P.14Getting a language model to return reliably structured data is not just about asking nicely. Here's the pattern that actually works at production scale.
P.15AI tools write code fast. TDD asks you to slow down and write tests first. These two impulses seem to be in tension. Here's how they actually work together.
P.16Clients ask agencies to 'do something with these PDFs' more often than you'd think. Here's how to actually build document extraction pipelines that work in production: OCR, vision models, and structured output.
P.17DuckDB runs OLAP queries directly in your process, on files on your laptop, without a server. Here's how to use it and when it beats spinning up BigQuery or Redshift.
P.18The command line tools most developers use today are a decade old. Here's what's replaced them and why: terminals, Git UIs, shell configuration, and the small tools that compound into significant time savings.
P.19Three leading agent orchestration frameworks, three different mental models. Here's when each one earns its place, what each costs you in complexity, and what the choice looks like when you're debugging at 2am.
P.20Python 3.13 shipped an experimental mode that removes the Global Interpreter Lock. Here's what the GIL actually does, what free-threaded Python changes, and what it still doesn't fix.
P.21Python 3.14 shipped in October 2025. Seven months in, the features worth caring about have become clear. Here's what changed, what to upgrade for, and what to ignore.
P.22Polars has hit stable 1.x and is showing up in production data pipelines everywhere. Here's an honest comparison with pandas, where Polars wins, where it doesn't, and a migration walkthrough.
P.23Background jobs that crash mid-execution lose all their state. Temporal solves this by making workflows durable state machines that survive process restarts, deploys, and outages. Here's what it looks like in TypeScript and Python.
P.24Unit tests tell you if your code does what you wrote. They don't tell you if your AI feature does what users need. Here's how to build an evaluation pipeline that catches the failures that matter before users do.
P.25Getting a language model to return valid, schema-conforming JSON is harder than it looks. Here's what works in production, from native structured output APIs to library-level validation.
P.26uv is a Python package manager written in Rust that handles dependencies, virtual environments, and Python version management. We've been using it across all our projects since early 2026. Here's what actually changed.
P.27Async Django is production-ready in 2026, but landmines still exist. Four real projects, benchmarks, and a framework for deciding when async actually helps.
P.28EditorPickWe rebuilt a React SPA as a Django + HTMX app. 847KB of JavaScript became 48KB. Here is every pattern, rough edge, and performance win from the migration.
P.29EditorPickWe migrated three production projects from Celery to Django's new Tasks framework. Two went smoothly. One was a disaster. Here is everything we learned.
P.30EditorPickThe network latency between your Django app and your FastAPI ML service is probably longer than inference itself. Here is how to serve models from Django directly.
P.31EditorPickMost DRF vs FastAPI comparisons are written by people who only use one. We ship both in production. Here is what actually matters and when to choose each.
P.32How we built an AI-powered interface that lets non-technical users query any database using plain English, eliminating SQL expertise requirements and democratizing data access.
P.33Step-by-step guide to configuring a complete full-stack development environment with Python, Django backend, and React frontend.
P.34Learn Python variables and data types — integers, floats, strings, booleans, lists, tuples, and dictionaries with clear examples.
P.35Perform EDA on the Boston Housing dataset using scatter plots and regression lines to identify relationships between features and housing prices.
P.36Master hyperparameter tuning with GridSearchCV using KNN, Random Forest, and SVM models with custom scoring functions.
P.37Build a sentiment analysis pipeline with text preprocessing, TF-IDF vectorization, and Multinomial Naive Bayes classification on Twitter data.
P.38Explore time series forecasting methods including ARIMA, exponential smoothing, and seasonal decomposition for real-world prediction tasks.
P.39Understand decision tree algorithms for classification and regression, their pros and cons, and build an Iris classifier with Python code.
P.40Learn SVM theory including hyperplanes and the kernel trick, then build a classifier on the breast cancer dataset using scikit-learn.
P.41Build a logistic regression model to predict diabetes outcomes using the Pima Indians dataset, covering sigmoid functions, feature scaling, and evaluation.
P.42Implement K-Nearest Neighbors classification using scikit-learn with data visualization, model training, and performance evaluation on real datasets.
P.43Understand the KNN algorithm — how it works, distance metrics, choosing K, and its applications in both classification and regression tasks.
P.44Compare Naive Bayes, SVM, Decision Tree, and Random Forest for email spam detection with a complete Python pipeline from data loading to evaluation.
P.45Master MAE, MSE, R², RMSE, accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices with formulas and Python code examples.
P.46Build an insurance cost prediction model using multivariate linear regression with one-hot encoding, evaluation metrics, and residual analysis.
P.47EditorPickMaster 7 encoding techniques for categorical variables — one-hot, label, dummy, binning, count, frequency, and target encoding with Python examples.
P.48Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
P.49Explore derivatives, integrals, multivariate calculus, optimization, and differential equations with Python implementations using SymPy and NumPy.
P.50Learn essential statistics concepts — mean, median, mode, variance, standard deviation, percentiles, quartiles, and z-scores with Python implementations.
P.51Understand vectors, matrices, transpose, inverse, determinant, trace, dot product, and eigenvalues with NumPy implementations for data science.
P.52EditorPickMaster SQL from basics to advanced — SELECT, JOIN, GROUP BY, ORDER BY, indexes, date functions, and more using SQLite with Python.
P.53Learn to create compelling data visualizations using Matplotlib and Seaborn — line plots, scatter plots, bar charts, histograms, heatmaps, and more.
P.54Master Pandas for data manipulation — reading data, selecting columns, grouping, merging DataFrames, handling missing values, and working with dates.
P.55Learn NumPy essentials — arrays, shapes, reshaping, slicing, stacking, broadcasting, universal functions, and image processing with practical examples.
P.56EditorPickA deep dive into Python lists, tuples, sets, dictionaries, and functions with comprehensive code examples and practical exercises.
P.57Learn Python fundamentals including identifiers, data types (int, float, str, list, tuple, set, dict), operators, and basic operations with hands-on examples.
P.58EditorPickA structured 100-day data science bootcamp roadmap covering Python, statistics, machine learning, deep learning, and real-world projects.
P.59EditorPickA comprehensive guide covering 10 regression types — linear, polynomial, logistic, ridge, lasso, elastic net, and more — with Python code examples and selection criteria.
P.60Master Python list comprehensions including syntax, filtering, nested comprehensions, and dictionary/set comprehensions with practical code examples.
P.61Learn OpenCV fundamentals including image I/O, pixel manipulation, color conversion, resizing, filtering, edge detection, and feature detection with SIFT and SURF.
P.62Understand the divide and conquer algorithmic paradigm through the maximum subarray sum problem, with Python implementation and step-by-step analysis.
P.63EditorPickA comprehensive guide to EDA covering visualization techniques, summary statistics, correlation analysis, data cleaning, PCA, anomaly detection, and feature engineering.
P.64EditorPickMaster bitwise operations, bitmasking, bit manipulation tricks, and bit-based algorithms for competitive programming and software engineering interviews.
P.65Build a CNN model using ResNet50 transfer learning to classify gender from eye images, covering data preprocessing, model architecture, and evaluation.
P.66Learn to predict stock prices using Long Short-Term Memory (LSTM) networks in Python with TensorFlow, from data preprocessing to building and evaluating the model.
P.67A hands-on guide to building a fruit image classifier using transfer learning with ResNet50 in TensorFlow and Keras, achieving high accuracy on the Fruits-360 dataset.