Subject
165 entries
Python
Bookmarks
git_bayesect: Bayesian git bisect for flaky tests
Bayesian git bisect for flaky tests. Finally a way to handle probabilistic failures that standard bisect can't.
AgentFlow: dependency graph orchestration for AI agents
Orchestrate thousands of AI agents as dependency graphs with parallel fanout and remote execution. Like make/Airflow but for LLM agents.
Dynamic Programming: QuantEcon textbook
QuantEcon's open-source Dynamic Programming textbook teaches the mathematical foundations of sequential decision-making with Python and Julia implementations. Covers Bellman equations, value function iteration, and stochastic control — the tools behind modern RL and macro models alike.
Deep-ML: LeetCode-Style Machine Learning Practice
Deep-ML is a free, open-source LeetCode-style platform for machine learning — coding challenges across ML fundamentals, deep neural networks, computer vision, and NLP, with in-browser Python execution and immediate test feedback.
annotateai
annotateai is a Python package that uses LLMs to automatically annotate research papers — highlighting key claims, methodology sections, results, and limitations. Useful for quickly mapping what a paper is about before reading it in full.
fast.ai
fast.ai is Jeremy Howard and Rachel Thomas's free deep learning course and library, famous for teaching neural networks top-down — use them effectively first, understand the math later. Widely credited with democratizing deep learning education.
SURD: Synergistic-Unique-Redundant Decomposition of Causality
SURD is a Python library for decomposing causality into synergistic, unique, and redundant components in complex systems — quantifying how different variables jointly contribute to predicting future states. Developed for turbulence analysis but applicable to any causal inference problem.
ai-llm-agent-solver: Autonomous Gandalf Challenge Solver
An LLM-powered agent that autonomously solves the Gandalf AI challenge — a prompt injection security game where you try to extract a secret password from a guarded AI. Uses OpenAI API and agent-based reasoning.
wat: Deep Inspection of Python Objects
wat is a Python deep inspection tool for exploring objects at runtime — type, value, methods, parent classes, source code, and signatures, all with one expression. Fills the gap between dir() and a full debugger.
FastUI: Python-Defined React UIs
FastUI is a Pydantic framework for building React-based web UIs entirely in Python — no JavaScript required. The backend defines UI structure as Pydantic models, and matching TypeScript interfaces render them. Now inactive.
pydantic-resolve: Hierarchical Data Fetching for Pydantic
pydantic-resolve is a Python library that eliminates N+1 queries through declarative resolve/post methods on Pydantic models. Automatically batches related data fetches and maps results back to parents, with optional GraphQL and MCP service generation.
Django Sessions
An article on Django sessions — how the session framework works, the available backends (database, cache, file, cookie), and how session data flows through requests. Django's session system is the foundation for authentication and stateful web behavior.
Batch classification with Instructor
An Instructor example showing how to classify multiple items in a single batched LLM call using Pydantic schemas — more efficient than sequential one-at-a-time classification, and type-safe by construction.
Neural Networks from Scratch in Python
Neural Networks from Scratch (NNFS) is a book by Harrison Kinsley and Daniel Kukiela that builds neural networks in pure Python with no frameworks — the go-to resource for understanding what's actually happening inside backpropagation and gradient descent.
TokenCost: LLM API Cost Calculator
TokenCost is a Python library from AgentOps that counts tokens and calculates USD costs for 400+ LLM models before making API calls. Keeps a live-updated pricing database so your cost estimates don't go stale when providers update pricing.
Lato: Python Microframework for Modular Monoliths
Lato is a Python microframework for building modular monoliths and loosely coupled applications — explicit module boundaries, dependency injection, and event-driven communication within a single process. A structured alternative to ad-hoc Django app organization.
Outlines: Guided Text Generation
Outlines is a Python library for guided LLM text generation — constraining model outputs to match JSON schemas, regex patterns, or context-free grammars. It was one of the first production-quality structured generation libraries before OpenAI's own structured outputs feature.
QLoRA Minimal: Fine-tuning Notebook
A minimal Jupyter notebook demonstrating QLoRA fine-tuning — strips away framework boilerplate to show the core mechanics of 4-bit quantized LoRA training. Good reference for understanding what QLoRA actually does at the implementation level.
From Python to Rust: YouTube Playlist
A YouTube playlist teaching Rust to developers who already know Python — bridges concepts across the two languages rather than teaching Rust from scratch. Useful for Python engineers who want to understand Rust's ownership and performance model.
Python Mocking Minimal Working Examples
A minimal working example (MWE) repository demonstrating Python mocking patterns with unittest.mock — covering patch, MagicMock, side_effect, and common gotchas. A practical reference for developers who know they should mock but keep forgetting the syntax.
agentaction: Action Chaining and History for LLM Agents
agentaction is a Python library for action chaining and history management in LLM agents — a lightweight abstraction for defining, executing, and tracking sequences of agent actions with persistent history. An early building block for agent frameworks.
pytudes: Peter Norvig's Python Skill-Building Programs
Peter Norvig's collection of Python programs of "considerable difficulty" designed to perfect specific programming skills — a curated library of well-crafted exercises in algorithms, puzzles, and problem-solving by one of the most respected programmers alive.
llm-reasoners: Advanced LLM Reasoning Algorithms
llm-reasoners is a library for advanced LLM reasoning algorithms — implementing Tree of Thoughts, RAP (Reasoning via Planning), and other structured reasoning approaches over standard chain-of-thought. Useful for research into how to get LLMs to reason more reliably on complex tasks.
Experimental Prompting DSL: Origin of Instructor
Jason Liu's experimental prompting DSL for structured LLM outputs via OpenAI function calling — an early version of what became the Instructor library. Shows the origin of the pydantic-based approach to reliable structured extraction from LLMs.
ChatGPT + Google Drive with LangChain
A tutorial for connecting ChatGPT to Google Drive using LangChain and Python in 30 lines of code — an early canonical example of the document Q&A pattern using LangChain's document loaders and retrieval chain.
AWS S3 Signed URLs in Django
A guide to generating AWS S3 presigned URLs in Django — allowing clients to upload or download files directly to S3 without routing through your application server. Standard pattern for handling file storage in Django applications at scale.
Guardrails: Adding Structure and Validation to LLM Outputs
Guardrails adds structural and semantic validation to LLM outputs — defining schemas for what the model should return and automatically retrying or correcting when outputs don't conform. An early solution to LLM output reliability before JSON mode and structured outputs became standard.
pygwalker: Pandas DataFrame to Tableau-Style UI
pygwalker turns a pandas DataFrame into a drag-and-drop Tableau-style visual analysis interface inside Jupyter notebooks — one line of code to replace writing matplotlib/seaborn boilerplate with interactive visual exploration. Strong tool for exploratory data analysis.
BERTopic: BERT-Based Topic Modeling
BERTopic is the leading open-source topic modeling library using sentence embeddings and clustering rather than word co-occurrence statistics — produces coherent, human-readable topics that LDA-style models often can't. The changelog tracks its evolution as the library added new backends and features.
redframes — Python Data Manipulation Library
redframes is a general-purpose Python data manipulation library that wraps pandas with a more consistent, readable API — aimed at making common data wrangling tasks less verbose without abandoning the pandas ecosystem.
Manifest: Prompt Programming with Foundation Models
Manifest is a Python library from Stanford's HazyResearch lab for prompt programming with foundation models — a unified interface across providers with caching, batching, and structured output support. An early formalization of LLM programming patterns before LangChain dominated.
GPT-3 + Python Interpreter (gpt.py)
A 2022 Replit demo by Sergey Karayev showing GPT-3 armed with a Python interpreter — doing exact arithmetic, making API requests, and answering questions that pure text generation gets wrong. An early, concrete preview of what LLM tool use would look like.
Calmcode: Calm Python Video Lessons
Calmcode.io is a platform of short, clear video lessons for Python tools and data science libraries — 757 videos across 106 courses, designed around reducing skill anxiety rather than maximizing content density.
100x Faster EVM Traces with Python
banteg's writeup on getting 100x faster EVM transaction traces using Python with an Erigon node backend — replacing slow Geth-based trace_* RPC calls with Erigon's streaming API. Essential reading for DeFi analytics and MEV research.
46-Page Guide to Pricing Options and Implied Volatility with Python
PyQuant News's 46-page guide to pricing options and calculating implied volatility in Python — covers Black-Scholes, Greeks, and the IV surface with working code. A self-contained practical reference for quant practitioners using Python.
ethereum-etl: Python ETL for Ethereum Blockchain Data
ethereum-etl is the standard Python library for extracting Ethereum blockchain data into structured formats — blocks, transactions, ERC-20 transfers, receipts, logs, internal transactions — with Google BigQuery export support. The reference implementation for blockchain data pipelines.
Eli Bendersky's Website
Eli Bendersky's personal blog is a deep-dive technical reference for systems programming, compilers, and Go/Python internals. His posts on how things work at the implementation level — parsers, ELF binaries, coroutines, LLVM — are consistently among the best on the internet.
Web Scraping Open Project
A GitHub repository collecting open knowledge about web scraping in Python — covering tools, techniques, anti-scraping countermeasures, and best practices. Community-built reference for practitioners building data collection pipelines.
Datasette: Architecture Notes on Simon Willison's Tool
Architecture Notes' deep dive on Datasette by Simon Willison — covering how a SQLite-based tool for instantly publishing databases as explorable web APIs became a beloved tool and a case study in opinionated small-tool philosophy. Willison's approach to software design is worth studying independently.
Scrapism: Web Scraping as Art and Activism
Scrapism is Sam Lavigne's guide to web scraping as artistic and activist practice — using Python scraping tools to collect and repurpose web data for critical or creative ends. Treats scraping as a form of political speech as much as technical skill.
Python Decorators for Data Scientists
Marton Trencseni's survey of Python decorators useful for data scientists — covering retry logic, timing, caching, and type checking patterns that bridge the gap between exploratory notebook code and production pipelines. Good reference for DS engineers who want production-grade patterns without abandoning Pythonic style.
Quant-MELO-Portfolio: Bayesian Portfolio Optimization
Quant-MELO-Portfolio is a Python project applying Bayesian architecture to stock portfolio optimization — finding optimal weights via Global Minimum Variance and Tangency portfolios. A concrete implementation of mean-variance optimization with Bayesian uncertainty quantification.
py-caskdb: Educational Disk-Based Key-Value Store
py-caskdb is an educational Python implementation of the Bitcask storage model — a log-structured, append-only disk-based key-value store. A hands-on way to understand how real KV stores like Riak's Bitcask backend achieve fast writes with crash safety.
Data Science with Python and Dask
Jesse Daniel's Manning 2019 book teaching Dask for parallel and out-of-core data science in Python — using familiar pandas-like DataFrames and numpy-like arrays across cores and machines. The go-to resource for scaling Python data science workflows beyond single-machine memory limits.
Ponder: Pandas at Scale
Ponder is a startup that makes Pandas run at scale without rewriting your code — a drop-in compatibility layer that runs standard Pandas operations on distributed backends. Targets the massive installed base of data scientists who know Pandas but hit its single-machine limits.
Scalene: High-Performance Python Profiler
Scalene is a high-performance Python profiler that measures CPU time, GPU time, and memory simultaneously with very low overhead — and attributes memory allocation and copy costs line-by-line. The best profiler for Python if you care about both speed and memory.
DeZero Book: Build a Deep Learning Framework from Scratch
DeZero is a book that builds a deep learning framework from scratch in pure Python — teaching automatic differentiation, computational graphs, and the internals of PyTorch/Chainer by implementing them. The most hands-on way to understand how deep learning frameworks actually work.
Portable Python Projects: Home Automation on Raspberry Pi
A Pragmatic Programmers book teaching home automation with Raspberry Pi and Python — projects like smart lighting, temperature monitoring, and voice control, most completable in an hour. Bridges the gap between Python programming and physical home control without requiring electronics expertise.
Production Code for Data Science: Our Experience with Kedro
Beamery's engineering team shares their experience using Kedro to bring software engineering discipline to data science code in production — covering what worked, what required adaptation, and how the pipeline structure changed their team's workflows.
Ploomber: Data Pipelines from Dev to Production
Ploomber is a Python framework for building data pipelines that can develop in Jupyter notebooks and deploy to Kubernetes, Airflow, or AWS Batch without rewriting code. Solves the notebook-to-production gap by treating notebooks as first-class pipeline tasks.
Kedro: Production-Ready Data Science Pipelines
Kedro is an open-source Python framework for building reproducible, maintainable, and modular data science pipelines — applying software engineering principles (catalogs, pipelines, project templates) to ML workflows. The answer to 'how do data science teams write production-grade code.'
ML Zoomcamp — Free Cohort Machine Learning Course
Alexey Grigorev's ML Zoomcamp — a free cohort-based machine learning course covering regression, classification, deployment, and MLOps fundamentals. A comprehensive practical curriculum from the author of Machine Learning Bookcamp.
Cookiecutter Data Science Template
Cookiecutter Data Science is a standardized, opinionated project template for data science work in Python — a sensible starting folder structure that makes projects reproducible and shareable. The de facto standard for organizing Python data science projects.
Python Packages of Interest — Tao of Mac
Rui Carmo's (Tao of Mac) curated list of Python packages of interest — a practitioner's annotated reference of useful, battle-tested Python libraries organized by domain. A complement to PyPI search for finding quality packages.
Tour of Python Itertools
Martin Heinz's comprehensive walkthrough of Python's itertools module — chaining, slicing, grouping, and combining iterators efficiently. One of the best references for using Python's built-in iterator toolkit before reaching for third-party alternatives.
Functools: The Power of Higher-Order Functions in Python
Martin Heinz's guide to Python's functools module — partial application, memoization via lru_cache, reducing sequences, and higher-order function patterns. The companion to itertools for functional-style Python.
Ray: A Distributed Framework for Emerging AI Applications
Micah Lerner's paper summary of Ray — UC Berkeley's distributed computing framework for AI workloads. Ray unified task-parallel and actor-based distributed computing in a Python-native API, becoming the foundation for libraries like RLlib, Tune, and later Ray Serve.
Django for Startup Founders
Alex Krupp's guide to Django architecture for SaaS founders — arguing against the default 'fat models' pattern in favor of service layers, clear separation of concerns, and patterns that scale with a small team. Opinionated and practical.
Peter Norvig's Probability Notebook
Peter Norvig's probability notebook from pytudes — a Jupyter walkthrough of probability theory using clean Python, demonstrating how to simulate and compute probabilities with elegant code. Companion to his broader skill-building notebook collection.
Hypermodern Python
Claudio Jolowicz's influential multi-part guide to modern Python project setup — covering pyenv, Poetry, Nox, pre-commit, and automated testing and linting. The 2020 standard for what a well-configured Python project looks like before Ruff and uv simplified things further.
Practical SQL for Data Analysis
Haki Benita's essay showing how SQL can replace Pandas for a surprising range of data analysis tasks — window functions, aggregations, pivoting, and data quality checks. Makes the case that analysts often reach for Python when SQL would be faster and simpler.
How to Make an Awesome Python Package in 2021
Anton Zhiyanov's practical guide to creating a well-structured Python package in 2021 — covering pyproject.toml, setup.cfg, versioning, testing, and publishing to PyPI. A concise alternative to piecing together the official documentation.
Rich: Python Library for Beautiful Terminal Output
Rich is Will McGugan's Python library for beautiful terminal output — syntax highlighting, markdown rendering, tables, progress bars, and formatted logging. Became the standard for making Python CLI tools and scripts look professional.
Scikit-Learn Cheat Sheet (2021)
A cheat sheet for scikit-learn's main API patterns — estimator interface, preprocessing, model selection, and pipelines. Useful for quickly recalling the consistent fit/predict/transform pattern across all sklearn objects.
Many Models Workflows in Python
Alex Hayes's port of the R 'many models' workflow pattern to Python — fitting many models across groups using tidy data conventions. Bridges the gap between R's purrr/broom/tidymodels idioms and Python's pandas/scikit-learn ecosystem.
Shapash: Making Machine Learning Models Transparent
Shapash is MAIF's Python library for making ML models interpretable to non-technical stakeholders — wrapping SHAP and LIME with better visualizations and business-friendly explanations. Targets the gap between data scientists and decision-makers.
FACET: Human-Explainable AI
FACET is BCG Gamma's Python library for human-explainable AI — extending SHAP with interaction effects and redundancy-aware feature importance, plus simulation tools for model-based what-if analysis. More sophisticated than vanilla SHAP for understanding feature relationships.
SHAP: SHapley Additive exPlanations
SHAP (SHapley Additive exPlanations) is the standard Python library for explaining individual predictions from any ML model using game-theoretic Shapley values. It works across tree models, deep neural networks, and linear models, and produces both local and global interpretability.
django-seal: Queryset Sealing for Django
django-seal lets you mark a QuerySet as 'sealed' so that any lazy evaluation attempt (iterating after the context is closed, triggering N+1 queries) raises an exception. It enforces eager loading discipline at the queryset level, catching ORM performance mistakes in development.
ThetaGang: Options Premium Collection Bot
ThetaGang is an open-source Python bot for Interactive Brokers that automates theta-positive options strategies — selling covered calls and cash-secured puts to collect premium. Designed for passive income from options rather than directional trading.
LazyPredict: Fit All scikit-learn Models in One Line
LazyPredict fits and evaluates all scikit-learn classifiers or regressors on a dataset with a single call, returning a sorted comparison table. A fast baseline scanner for figuring out which model family is worth investing in before tuning.
skift: scikit-learn Wrappers for fastText
skift wraps Facebook's fastText text classifiers in scikit-learn's estimator API, making fastText accessible as a drop-in component in scikit-learn pipelines and GridSearchCV. Useful for fast, production-grade text classification without leaving the sklearn ecosystem.
scrapy-history-middleware: S3 Historical Cache for Scrapy
A Scrapy middleware that stores every crawled response in S3, building a historical archive of web resources over time. Enables point-in-time replay of crawls and separates the concerns of fetching from processing.
Sparse Matrices in SciPy
A visual explainer of sparse matrix formats in SciPy (COO, CSR, CSC, LIL, DOK) with animated illustrations showing how data is stored. Essential reading before working with high-dimensional feature matrices in ML or graph algorithms.
Machine Learning Mastery
Machine Learning Mastery is Jason Brownlee's prolific tutorial blog — hundreds of practical, code-first ML tutorials covering scikit-learn, Keras, time series, NLP, and more. Known for quantity and accessibility rather than depth, but a go-to reference for how-to implementations.
Using Python's bisect Module
A practical walkthrough of Python's bisect standard library module — binary search for maintaining sorted lists and efficient data binning. Covers two key use cases with code examples.
PandasGUI — A GUI for Pandas DataFrames
PandasGUI is a graphical interface for exploring and visualizing pandas DataFrames — drag-and-drop CSV import, interactive filtering, statistical summaries, and plot creation without writing code. Useful for rapid EDA.
Jupyter Notebooks Gallery — notebook.community
notebook.community is a curated gallery of publicly shared Jupyter notebooks — a discovery layer for interesting notebooks covering machine learning, data analysis, visualization, and scientific computing. Good for finding worked examples.
ELI5 — sklearn Explainability Module
ELI5's sklearn module provides model explanation tools for scikit-learn estimators — feature importance, prediction decomposition, and permutation-based importance across linear models, tree ensembles, and SVMs. The explainability companion for sklearn workflows.
sktime — Unified Machine Learning with Time Series
sktime is a Python library providing a unified scikit-learn-compatible interface for time series machine learning — forecasting, classification, regression, clustering, and anomaly detection. Solves the ecosystem fragmentation problem for temporal data.
fastcore — Python Extensions by fast.ai
fastcore is fast.ai's Python utility library extending the standard library with mixins, delegation, functional programming patterns, and parallel processing helpers. Used heavily in fast.ai's deep learning courses and libraries.
Airflow and XCom: Inter-Task Communication Use Cases
A guide to Airflow's XCom (cross-communication) mechanism for passing data between tasks in a DAG — covering when to use it, when to avoid it, and practical use cases. XCom is one of the most misused Airflow features.
150+ Best Machine Learning, NLP, and Python Tutorials
Robbie Allen's curated list of 150+ machine learning, NLP, and Python tutorials, organized by topic and difficulty. A high-signal link aggregation for 2020-era ML self-study, with commentary on what each resource covers.
Comprehensive Python Cheatsheet
A comprehensive, single-page Python reference covering everything from basic syntax to advanced features like decorators, generators, and metaclasses. Unusually complete and well-organized for a cheatsheet — more like a structured quick-reference manual.
PRML: Pattern Recognition and Machine Learning Algorithms in Python
Python implementations of algorithms from Bishop's 'Pattern Recognition and Machine Learning' — the canonical probabilistic ML textbook. Bridges the gap between the math in the book and working code.
Practical Deep Learning for Coders — fast.ai
fast.ai's Practical Deep Learning for Coders — Jeremy Howard and Rachel Thomas's free course that inverted the standard pedagogy: start with working image classifiers, then learn the theory underneath. Democratized deep learning at a moment when most education assumed a PhD on-ramp.
Over 150 of the Best Machine Learning, NLP, and Python Tutorials
A 2017 curated list of 150+ ML, NLP, and Python tutorials organized by topic — a snapshot of the best practitioner learning resources from the pre-transformer era. Useful as a historical reference for what the field considered canonical learning material at the time.
Dive into Machine Learning
A curated GitHub guide for learning machine learning hands-on with Jupyter notebooks and scikit-learn — one of the most-starred beginner ML resources of 2015. Its strength is pairing interactive notebooks with curated external readings rather than building yet another tutorial from scratch.
benchm-ml: ML Algorithm Benchmark Comparison
A systematic benchmark of machine learning algorithms across platforms and implementations — comparing gradient boosting, random forests, neural networks, and others on speed and accuracy. One of the best empirical references for choosing between ML tools in 2015, when the xgboost vs sklearn debate was live.
Rodeo: A Data Science IDE for Python
yhat's 2015 launch of Rodeo — a Python IDE built for data science workflows, modeled on RStudio's four-pane layout. It didn't outlast the market consolidation around Jupyter Lab and VS Code, but its design assumptions turned out to be right.
Notebook Gallery: Best IPython Notebooks
A curated gallery of the most-viewed IPython/Jupyter notebooks — an early community resource for discovering high-quality notebook examples across ML, data analysis, and scientific computing. Predecessor to nbviewer and the current ecosystem of notebook sharing platforms.
Top Mistakes Developers Make When Using Python for Big Data Analytics
A practical rundown of the top Python performance mistakes for big data workloads — covering generator vs. list comprehension choices, pandas anti-patterns, and when to reach for NumPy. Still relevant since Python's core performance traps haven't changed.
Pandas Pivot Table Explained
A step-by-step tutorial on using Pandas pivot tables for business data analysis from the Practical Business Python blog. Pivot tables are the single most useful tool for quickly summarizing and reshaping tabular data, and this covers the full API clearly.
Simple CSV Data Wrangling with Python
District Data Labs tutorial on CSV data wrangling with Python, covering the basics of loading, cleaning, and transforming tabular data before analysis. A foundational skill that every data scientist spends far more time on than they'd like.
Seaborn — Plotting Distributions Notebook
Seaborn's official distribution-plotting example notebook — demonstrates the library's statistical visualization API for histograms, KDE plots, rug plots, and joint distributions. The reference for anyone learning to visualize data distributions in Python.
Lea — Discrete Probability Distributions in Python
Lea is a Python library for working with discrete probability distributions symbolically — defining distributions, computing joint and conditional probabilities, and simulating outcomes. An unusual tool that treats probability as a first-class programming construct.
Yelp Pyleus — Apache Storm Topologies in Pure Python
Yelp open-sources Pyleus — a framework for writing Apache Storm stream processing topologies in pure Python. Solved the JVM barrier that kept Python data engineers from using Storm's real-time streaming capabilities.
Pykov — Finite Markov Chains in Python
Pykov is a small Python library for working with finite regular Markov chains — define chains from scratch or load from files, compute stationary distributions, simulate walks, and analyze steady-state behavior. Useful for any system that can be modeled as probabilistic state transitions.
Kernel PCA
Sebastian Raschka's tutorial on Kernel PCA — extending standard PCA to capture non-linear structure using the kernel trick with RBF kernels. Includes Python implementation, making it one of the clearest practical explanations of the technique available in 2014.
Crunchbase Network Analysis with Python
A Zipfian Academy alumnus's network analysis of the Crunchbase investment graph in Python — using graph centrality measures to identify influential investors and startups. An early example of applying graph algorithms to startup ecosystem data.
Exploratory Computing with Python — Mark Bakker
Mark Bakker's open course on exploratory computing with Python — Jupyter notebooks covering NumPy, Matplotlib, and scientific computing techniques. Aimed at engineers and scientists who want to use Python for quantitative analysis without a computer science background.
Scikit-learn Pipelines and FeatureUnions
Zac Stewart's deep dive into composing scikit-learn Pipelines and FeatureUnions — showing how to chain preprocessing steps, branch feature transformations, and combine them back together while preventing data leakage. The definitive 2014 guide to production-ready sklearn code.
Getting the Best Performance out of NumPy
Featured recipe from the IPython Cookbook on getting the best performance out of NumPy — covering vectorization, broadcasting, memory layout, and avoiding Python loops. The kind of practical optimization guide that separates slow scientific Python from production-grade numerical code.
Bayesian Regression with PyMC: A Brief Tutorial
A Zipfian Academy student's tutorial on Bayesian linear regression using PyMC — notable both as an accessible introduction to probabilistic modeling and as a window into the Zipfian cohort's learning culture of public writing. PyMC was the dominant Python tool for Bayesian modeling at the time.
Pelican + PlanOut: A/B Testing on a Static Site
Trent Hauck's post combining Facebook's PlanOut experiment framework with the Pelican static site generator — a creative integration showing how to run A/B tests on a static site without server-side logic. An early example of bringing rigorous experimentation tooling to lightweight web stacks.
Hadoop, Python, and NoSQL Lead the Pack for Big Data Jobs
InfoWorld's 2014 analysis of job postings showing Hadoop, Python, and NoSQL as the top skills in big data job listings — a snapshot of the technology bets companies were making at the height of the big data boom.
PyData 2013 — Martin Laprise
Martin Laprise's PyData 2013 talk materials — a conference covering Python tools for data analysis, machine learning, and scientific computing. PyData was (and remains) a key community venue for the Python data ecosystem.
IPython Notebook — msund Gist
An IPython notebook shared via gist by msund — likely conference or tutorial materials from the 2014 Python/data science community. Saved without content, context inferred from surrounding bookmarks in the same PyData period.
Up and Down PyData 2014 — Rob Story
Rob Story's PyData SV 2014 talk notebook on 'Up and Down' — covering the landscape of Python data visualization tools from low-level (Matplotlib) to high-level (Bokeh, Vincent, Folium). A snapshot of the visualization stack debate of that era.
Functional Performance with Core Data Structures — PyData SV 2014
Matthew Rocklin's PyData SV 2014 talk on functional performance with core data structures — showing how functional programming patterns and Python's built-in data structures enable high-performance computation without reaching for C extensions.
Learn Pandas — IPython Notebook Tutorial Series
Bitbucket-hosted IPython notebook series for learning Pandas from scratch — one of the early hands-on Pandas tutorials when official documentation was sparse. Covers data loading, manipulation, groupby, and time series.
Converting Categorical Data into Numbers with Pandas and Scikit-Learn
FastML tutorial on converting categorical variables to numeric form using Pandas and scikit-learn's LabelEncoder and OneHotEncoder. A foundational data preprocessing step that trips up many beginners.
Parsing English with 500 Lines of Python
Matthew Honnibal's post describing a fast dependency parser for English implemented in 500 lines of Python — a precursor to spaCy. Demonstrates that a useful NLP system doesn't need a massive codebase if the algorithm is right.
How to Get Started with Machine Learning in Python
Prismatic story aggregating a 'How to Get Started with Machine Learning in Python' tutorial — a 2014 entry point to scikit-learn, NumPy, and Pandas for ML practitioners. Reflects the era's onboarding gap before dedicated ML courses existed.
The Flask Mega-Tutorial, Part III: Web Forms
Part III of Miguel Grinberg's Flask Mega-Tutorial — covers web forms using Flask-WTF and WTForms. The Mega-Tutorial was the canonical resource for learning Flask web development in 2014, and this forms chapter is where most projects got real.
MH370 MCMC Notebook — Conor Myhrvold
Conor Myhrvold's IPython notebook using Monte Carlo simulation to analyze the probable flight path of MH370 from satellite pings. An early high-profile example of using Bayesian inference and simulation for real-world analysis.
My Favorite 7 IPython Notebooks
A curated list of seven standout IPython Notebooks shared in early 2014 — when the notebook format was the primary vehicle for sharing data science work and reproducible analysis. Reflects the community's excitement about executable, shareable computation.
Frequentism and Bayesianism: A Practical Introduction
Jake VanderPlas's Python-driven comparison of frequentist and Bayesian statistics — showing the two philosophies side-by-side with code. The most-cited accessible treatment of a distinction that confuses most practitioners.
Scikit-Learn: Model Validation and Testing (PyCon 2013 Notebook)
Jake VanderPlas's PyCon 2013 notebook on model validation and testing in scikit-learn — covers train/test splits, cross-validation, and model selection in executable notebook form. A practical tutorial that shaped how Python practitioners learned to evaluate models.
Some Useful Machine Learning Libraries
A 2013-era survey of machine learning libraries across Python, R, Java, and C++ — a snapshot of the fragmented ML tooling landscape before scikit-learn and deep learning frameworks consolidated the field.
30 Python Language Features and Tricks You May Not Know About
Sahand Saba's tour of 30 Python language features that intermediate programmers often overlook — covers unpacking, generators, context managers, decorators, and more. A practical companion to reading the Python docs.
Random Sampling from Very Large Files
Practical techniques for taking random samples from large files without loading them into memory — covering Unix tools (shuf, awk) and reservoir sampling. Essential for working with data too large for pandas to read in one shot.
Python Best Practice Patterns (Vladimir Keleshev — Notes)
Steven Loria's notes from Vladimir Keleshev's talk on Python best practice patterns — covers protocol classes, named tuples, reducing coupling, and using Python's data model idiomatically. Practical design advice for writing maintainable Python.
100 Numpy Exercises
Nicolas Rougier's 100 exercises for NumPy, ranging from beginner to expert, covering the array operations that make NumPy indispensable. One of the most effective ways to internalize NumPy's vectorization mindset.
Research Computing Meetup Fall 2013
GitHub repo of materials from the Research Computing Fall 2013 meetup series — IPython notebooks covering Python for scientific computing, parallel processing, and HPC workflows.
A Gallery of Interesting IPython Notebooks
Curated GitHub wiki of interesting IPython Notebooks covering scientific computing, data analysis, machine learning, and visualization. The 2014 canonical list of notebooks worth running — before nbviewer and Binder made sharing notebooks routine.
Data Science in Python — Yhat Tutorial
Yhat's end-to-end data science tutorial in Python using pandas for data manipulation and scikit-learn for modeling. One of the cleaner introductory pipelines from 2014, before this kind of content became ubiquitous.
The Homogenization of Scientific Computing: Why Python Is Eating Other Languages' Lunch
R-Bloggers post arguing that Python was converging on R and MATLAB's territory in scientific computing in 2014. The key insight: Python didn't win by being better at any one thing, but by being good enough at everything while sharing one ecosystem.
R vs Python — Round 1
The Swarm Lab's side-by-side comparison of R and Python on a data analysis task — first in a series. Both languages solve the same problem, revealing stylistic and ecosystem differences rather than a clear winner.
An Easy Way to Bridge Between Python and Vowpal Wabbit
Steve's Machine Learning Blog post on using Python to feed data to Vowpal Wabbit via subprocess — a simple bridge for calling VW from Python workflows without a native binding.
Statistical Analysis Made Easy in Python
Randy Olson's tutorial on statistical analysis in Python using SciPy stats and pandas — t-tests, ANOVA, chi-squared, and more. A practical bridge from R's built-in stats to Python's ecosystem in 2012.
A Pandas Cookbook — Julia Evans
Julia Evans's hands-on pandas cookbook — eight chapters of real-dataset exercises covering groupby, merging, text ops, and timestamp handling. The go-to resource that made pandas approachable before the official docs caught up.
A Not-So-Basic Neural Network in Python
Daniel Rodriguez's practical tutorial on implementing a non-trivial neural network in Python from scratch — going beyond the toy perceptron examples to show backpropagation and training on real data. A mid-2013 hands-on coding reference.
Gmail No Response — Follow Up on Unanswered Emails
Jonathan Kim's Gmail script to identify emails you sent that never received a reply — queries the Gmail API for sent messages with no subsequent reply thread. A small automation that solves a real followup problem before commercial tools like Boomerang addressed it.
Advanced Data Structures in Python
Pypix overview of advanced data structures in Python beyond the built-in list/dict/set — covering heaps, tries, segment trees, and other structures that Python's standard library either implements partially or not at all.
Teaching a Computer to Read: NLP Hacking in Python
Scripted blog's introduction to NLP in Python — using NLTK for tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis. A practical hands-on introduction to text processing written for a content-tech company's engineering blog.
Introduction to Recommendations with Map-Reduce and mrjob
Tutorial on building item-based collaborative filtering recommendation systems using MapReduce and Yelp's mrjob Python library. Shows why distributed computation is necessary for large-scale similarity calculations.
Python Displacing R As The Programming Language For Data Science
ReadWrite article on Python displacing R as the primary data science language — part of the 2013 wave of coverage tracking the Python/R competition. Python's software engineering strengths and growing ML ecosystem were tipping the balance.
How Python Became the Language of Choice for Data Science
Mikio Braun's account of how Python displaced MATLAB as the data science language of choice — tracing the inflection point to 2005 licensing changes and a pivotal NIPS satellite workshop. The story behind what's now taken for granted.
prettyplotlib: Painlessly Create Beautiful Matplotlib Plots
Olga Botvinnik's prettyplotlib — a Python library that wraps matplotlib with better defaults (ColorBrewer palettes, no chartjunk) to produce publication-quality plots without manual style configuration. The answer to 'why does matplotlib look so bad by default?'
Bayesian Statistical Analysis with PyMC
PyTennessee 2013 presentation on Bayesian statistical analysis with PyMC — introducing probabilistic programming in Python as a practical alternative to frequentist methods. PyMC let practitioners write down generative models and get MCMC inference without implementing samplers from scratch.
Intro to pandas Data Structures
Greg Reda's introduction to pandas data structures — Series, DataFrame, and Index — written in 2013 when pandas was still new enough to need a clear on-ramp. A canonical early tutorial that helped many data scientists learn the library.
Parakeet: A Faster Python for a Better Tomorrow
Parakeet was a Python JIT compiler targeting NumPy array operations, promising to make numerical Python code run at near-native speeds without rewriting in C or Cython. An early attempt at the problem that Numba later solved more completely.
sklearn-pandas: Bridge Between pandas and scikit-learn
sklearn-pandas is a library bridging pandas DataFrames and scikit-learn's pipeline API — enabling column-level transformations with named features rather than anonymous numpy arrays. Fills a friction point that frustrated every data scientist using both libraries together.
Python Extensions to Do Machine Learning
A roundup of Python extensions and libraries for machine learning circa 2013 — the ecosystem before it had fully consolidated around scikit-learn, NumPy, pandas, and matplotlib as the canonical stack.
Getting Started With Python For Data Science (Kaggle)
Kaggle's Getting Started With Python For Data Science guide — a practical on-ramp covering the core libraries (NumPy, pandas, matplotlib, scikit-learn) oriented around Kaggle competition workflows. The canonical starting point for competition-driven ML learning.
Teaching a Computer to Read: NLP Hacking in Python
Scripted's NLP hacking tutorial in Python — covering tokenization, part-of-speech tagging, named entity recognition, and text classification with NLTK and scikit-learn. An applied introduction to NLP for data scientists.
Quantitative Economics (quant-econ.net)
quant-econ.net is Thomas Sargent and John Stachurski's free online course in quantitative economics using Python — covering dynamic programming, stochastic processes, and economic modeling. Nobel laureate-authored open curriculum before that was common.
Estimating User Lifetimes with PyMC
yhat's tutorial on estimating customer lifetimes with PyMC using Bayesian survival analysis — fitting probabilistic churn models to get full posterior distributions over lifetime value rather than point estimates. An early example of applied Bayesian modeling in Python before PyMC3 existed.
Python Quirks
LShift's catalog of Python behaviors that surprise experienced programmers — mutable default arguments, late-binding closures, class variable vs instance variable traps. The kind of language-design decisions that seem reasonable in isolation but bite you in real code.
Python and Real-time Web
Technical overview of real-time web patterns in Python circa 2013 — comparing Tornado, Twisted, gevent, and the emerging WebSocket ecosystem. A snapshot of Python's async web story before asyncio standardized the concurrency model.
Untrod Blog
The Untrod blog — a Python and Django-focused technical blog by Chris Clark. Pinned as a reference for practical Python web development posts in 2013.
Bayesian Methods for Hackers
Cameron Davidson-Pilon's open-source book teaching Bayesian inference through computational examples in Python, using PyMC3 for probabilistic programming. The approach is computation-first rather than math-first — ideal for programmers who want to apply Bayesian reasoning without heavy statistics background.
Parameter Optimization with Zipline, PiCloud, StarCluster, and IPython Parallel
Quantopian's blog post on running parameter optimization for trading strategies using Zipline backtester on PiCloud, StarCluster, and IPython Parallel — a 2013 example of cloud-distributed backtesting before it was a product. Shows the DIY infrastructure that Quantopian later packaged into their platform.
Quandl Python API
Quandl's Python API documentation — the quandl Python library that let researchers pull financial and economic time-series data directly into pandas DataFrames with a single function call. The key practical interface for data scientists using Quandl.
Natural Language Processing with Apache Hadoop and Python
Cloudera's 2010 post (bookmarked in 2013) on running natural language processing pipelines with Apache Hadoop and Python using Hadoop Streaming — an early recipe for scaling NLP beyond single-machine limits using commodity clusters.
Speeding Up Your Python Code
Max Burstein's practical guide to Python performance — profiling-driven optimization covering list comprehensions, generators, local variable access, string concatenation, and C extension use. A good 2013-era reference for the pragmatic Python performance techniques.
Getting Started with Python for Data Scientists
Data Community DC's guide to getting started with Python for data scientists — the standard 2013 on-ramp to scientific Python covering NumPy, Pandas, matplotlib, and scikit-learn. Represents the moment when Python decisively won the data science language wars.
Pandas and Python: Top 10
Manish Amde's top 10 Pandas techniques for data scientists — written in March 2013 when pandas was still a young library (0.10.x era). Captures the practical workflows that made pandas the dominant tool for tabular data manipulation in Python.
Blaze: A Python Compiler for Big Data
Continuum Analytics' announcement of Blaze — a Python compiler and array expression system designed to scale NumPy-style computations beyond in-memory datasets. An early attempt to bring Python's scientific computing ecosystem to big data before Spark/Dask became dominant.
Improving Your Python Productivity
Oz Katz's guide to Python productivity improvements — covering virtual environments, IPython, better REPL workflows, and tooling choices. A 2012-era reference that established many practices still standard today.
7 Python Libraries You Should Know About
A 2012 roundup of seven Python libraries worth knowing — a snapshot of which tools the Python community considered essential before the data science wave fully landed. Useful as a historical marker for the state of the Python ecosystem.
A Few Things to Remember While Coding in Python
A practical reference post on Python idioms and gotchas — covering mutable default arguments, list comprehensions, generators, decorators, and other patterns that distinguish experienced Python code from novice code.
Deploying Flask with uWSGI and nginx on Ubuntu
A 2012 tutorial on deploying Flask applications behind nginx using uWSGI as the application server on Ubuntu. The canonical stack for Python web apps before Docker and cloud deployment made it simpler — and still the right choice for self-hosted production Flask.
Python Decorators: A Primer
A practical explainer on Python decorators — the @syntax wrapper pattern that lets you modify or extend function behavior without changing the function itself. A key Python metaprogramming tool used for logging, auth, caching, and more.