James Powell
where else to find them
what they talk about
all 14 in the filters
where they keep going back
- PyData London6 editions2016–2024
- PyData NYC4 editions2018–2024
- PyData Berlin4 editions2014–2018
all 17 in the filters
on a stage with
- Brandon Burroughsonce
- Daniel Allanonce
- Tom Reillyonce
who talks about the same things
- Christopher Neugebauer29 talks
- Trey Hunner23 talks
- Reuven M. Lerner24 talks
- Łukasz Langa15 talks
- Harry Percival10 talks
- Larry Hastings9 talks
| title | conference | date |
|---|---|---|
| Can You Tell the Truth by Lying? | PyData NYC 2024 | 2024-11-06 |
Argues that precise truths often hide usefulness so pragmatic simplifications win: uses concrete Python examples — decorators (contrasting the lie decorator is… | ||
| How Dimensional is a `pandas.DataFrame`, anyway? | PyData Amsterdam 2024 | 2024-09-18 |
Claim: a pandas.DataFrame is one-dimensional, an index-aligned collection of Series, not a true 2D array. Demonstrations show Series as strictly homogeneous… | ||
| Code Reviews are Mostly Useless | Code Quality Conference 2024 | 2024-06-27 |
Claim: code reviews are mostly useless because cultural failures turn them into five-line review nitpicks while a 10,000-line no-review sails through… | ||
| Are generator-coroutines really the answer? | PyData London 2024 | 2024-06-14 |
Claim: generator-coroutines let code linearize state machines and externalize stopping criteria, but practical costs appear: Python ≥3.6 async/await and yield… | ||
| Simple Simulators with pandas and Generator Coroutines | PyData NYC 2023 | 2023-11-01 |
Builds a backtester that actually runs in Python using plain pandas Series inputs/outputs and generator coroutines to model strategy and simulator loops… | ||
| Keynote Speaker – Python Steering Council | PyCon US 2023 | 2023-04-19 |
The Python Steering Council states concrete changes: F-strings moved into parser for measurable speed and proper nesting, an experimental per-subinterpreter… | ||
| Keynote Speaker – James Powell | PyCon US 2023 | 2023-04-19 |
Argues that replacing a monolithic scipy.optimize.newton API with an explicit generator yields concrete gains: fewer combinatorial keyword arguments, far fewer… | ||
| Why do I need to know Python- I'm a pandas user | PyData NYC 2022 | 2022-11-09 |
Argues that concrete Python knowledge pays off: using contextlib.closing fixes openpyxl file locks, pathlib + regex picks CSVs reliably, and itertools.product… | ||
| Whatever I Can do to Entertain You in 30 Minutes | PyData London 2022 | 2022-06-17 |
Laptop fails to boot so notes are gone and a short live coding session runs on a terrible keyboard; claim: generators for simulation control are the right… | ||
| What Does It All Really Mean | PyCon India 2020 | 2020-10-02 |
Argues that tiny syntactic choices convey human-intent and change tooling choices, then demonstrates it by digging into CPython and ecosystem details… | ||
| Furious & Fast Python 7: Writing Fast Python Code | PyData Fest Amsterdam 2020 | 2020-06-15 |
Claims a concrete M-I-S-S-I framework (Measurement, Intuition, Structure, Specialization) and shows tools and data: benchmarks with scalene and pyspy reveal… | ||
| Objectionable Content | PyData Austin 2019 | 2019-12-07 |
Clear claim: use the Python object model to build production systems (not ML), with concrete patterns and tradeoffs. Demos include a resistor-network builder… | ||
| What You Got Is What You Got | PyData Los Angeles 2019 | 2019-12-03 |
Argues that numpy pandas and networkx are powerful but hard to embed in production: ndarray are contiguous unboxed machine types so they give speed and simple… | ||
| Sloth & ENVy | PyData New York City 2019 | 2019-11-04 |
Explains how the process entry from the system V ABI threads a raw envp of null‑terminated strings through libc’s start_main into Python’s posix layer, how… | ||
| Because You Can Run, You Can't Hide: Some Musings on API Design | PyData London 2019 | 2019-07-12 |
Claim: Python’s runtime makes true information hiding infeasible. Demonstrations use inspect.getsource and disassembly to copy functions and swap… | ||
| Why do you have to make it so complicated? | PyData DC 2018 | 2018-11-16 |
A practical framework and two‑page handout maps concrete tradeoffs: when code moves from notebook→module→package to multi‑repo; when to adopt git, GitHub, git… | ||
| I Just Inherited 50,000 Lines of Code! What Now? — A Practical Guide | PyData Los Angeles 2018 | 2018-10-17 |
Claims concrete tactics for inheriting 50,000 lines: find runnable entry points, run c-loc and tests, expect many tests to fail, treat ~20% of code as… | ||
| … | PyData NYC 2018 | 2018-10-11 |
Shows that Python accepts surprising Unicode identifier characters by inspecting CPython tokens, the grammar, PEP 3131, Unicode DerivedCoreProperties and… | ||
| Let's SQL Like It's 1992! | PyData Berlin 2018 | 2018-07-06 |
Models a turn based space game in SQL and shows concrete results using Postgres with a user plugin that implements full temporality so updates and deletes… | ||
| Dynamic Code Instrumentation with Hacked Interpreters | PyData Amsterdam 2018 | 2018-05-25 |
Shows a working patched CPython that instruments every bytecode push by overriding the push macro, records when specific objects are touched, and emits JSON… | ||
| More About Generators | PyData London 2018 | 2018-04-26 |
Generations of Python features start with PEP-255 and PEP-342 and the claim here is concrete: generators act as coroutines and sequencing primitives but rarely… | ||
| So you want to be a Python expert? | PyData Seattle 2017 | 2017-06-30 |
Live demos on Python 3.6 implement concrete behaviors: add a class with __init__, __repr__, __add__, __len__ and it prints and adds as expected; disassemble… | ||
| Advanced Metaphors in Coding with Python | PyData Berlin 2017 | 2017-06-23 |
Compressed, code-first walkthrough of how Python features actually get used: concrete demos of metaclass hooks including the build_class hook and how ABCMeta… | ||
| Building Web-based Analysis & Simulation Platforms | PyData London 2017 | 2017-05-05 |
Live code shows a working pipeline: a Numpy Monte Carlo integrator (used to approximate π, ~10 million samples ~10s) is wrapped as a Python script, exposed… | ||
| Design Principles | PyData DC 2016 | 2016-10-07 |
Argues that complexity needs concrete justification and offers a usable framework called a complexity budget that trades user sophistication and user care to… | ||
| Hadoop in the cloud: A Nielsen use case | Strata + Hadoop World 2016 - New York, New York | 2016-09-27 |
Nielsen moves core analytics to public clouds to drive agile devops and elastic, near‑real‑time processing, running about 10% of commercial workloads in the… | ||
| Principles of Reporting Systems: It's About Time we Talked About Bitemporality | PyData Carolinas 2016 | 2016-09-14 |
Argues that bitemporal/full temporality makes reports reproducible and provable: capture both natural time and observation time so reruns explain value… | ||
| Don't Live Patch Your CPython Interpreter | PyData Chicago 2016 | 2016-08-26 |
A pip‑installable C extension called ratch live‑patches CPython by assembly‑trampolining the py_eval_frame_x loop and injecting callbacks (get_rat/set_rat) so… | ||
| The "NumPy" Approach | PyData San Francisco 2016 | 2016-08-12 |
Shows a concrete performance claim: rewriting a binary genetic algorithm from Python lists and explicit loops into NumPy arrays and vectorized ops cuts runtime… | ||
| What's New in Python 3 | PyData San Francisco 2016 | 2016-08-12 |
Slides list concrete Python 3.5–3.6 changes: PEP-based survey finds ~42 relevant PEPs, shows matrix @ operator implemented and used by numpy, general unpacking… | ||
| Overview of the NumFOCUS Data Science Stack | PyData San Francisco 2016 | 2016-08-12 |
A concrete inventory of what NumFOCUS actually funds and why: lists of Jupiter notebook and numpy and libraries (pandas, matplotlib, scikit-learn, xray… | ||
| The "Kwargh!" Problem | PyData Berlin 2016 | 2016-05-20 |
Claim is that Python is essentially impossible to fully statically analyze but useful heuristics can recover many forwarded keyword args. Uses inspect and ast… | ||
| Python and Johnny Cash | PyData London 2016 | 2016-05-06 |
Claims quick C‑Python hacks: AST-literals, resurrecting the print statement, lambda‑fusion. Demo builds an LD audit shim (≈10 lines of C glue) that embeds a… | ||
| `from __past__ import print_statement`: a Dadaist Rejection of Python 2 vs 3 | PyData Amsterdam 2016 | 2016-03-12 |
Presents a working, pip-installable hack that patches a running CPython by inserting a runtime C-Python trampoline to override eval behavior, plus a module… | ||
| Generators Will Free Your Mind | PyData Berlin 2014 | 2014-07-25 |
Claims that generators remove API-mode assumptions and avoid needless work with lazy streams. Demos show a 50-line meeting scheduler, a trading-query case… | ||
| nothing matches | ||