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Python Bytes

Python Bytes

Written by: Michael Kennedy and Calvin Hendryx-Parker
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Python Bytes is a weekly podcast hosted by Michael Kennedy and Calvin Hendryx-Parker. The show is a short discussion on the headlines and noteworthy news in the Python, developer, and data science space.Copyright 2016-2026 Politics & Government
Episodes
  • #496 A lake house in Seattle

    #496 A lake house in Seattle
    Sep 15 2026
    Topics covered in this episode: Pandas Should Go ExtinctPydantic-pint puts real-world units in your Pydantic modelsHow Libraries Run Rust Inside Python (With PyO3)AWS acquires DuckLabsExtrasJokeWatch on YouTube Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedInCalvin: Mastodon / BlueSky / X / LinkedInShow: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Pandas Should Go Extinct Pandas' slowness pushes teams toward "Big Data" tools (Spark, Databricks) they don't actually need — most workloads never hit true Big Data scaleAmazon Redshift telemetry: ~95% of tables are under 100GB, ~87% of queries touch 80GB or less — that's "Medium Data," not Big DataPolars and DuckDB fill that gap: single-machine, fast, no cluster required1 Billion Row Challenge benchmark: Pandas took 4m28s vs. Polars 5.04s and DuckDB 5.19s — DuckDB also used 19x less memoryOn a real-world NYC taxi dataset (3GB parquet), pure DuckDB ran 2x faster than pure Pandas while using a fraction of the RAMBonus: Apache Arrow lets you pass data between Pandas/Polars/DuckDB with zero copying, so trying them out doesn't mean a full rewrite Michael #2: Pydantic-pint puts real-world units in your Pydantic models Pydantic-pint bridges Pydantic and Pint so models can validate physical quantities like 4m or 12 meters instead of bare floats. Fields annotated with PydanticPintQuantity parse user input, convert between compatible units, and serialize quantities back out as strings. That closes a real gap for anything consuming API payloads, config files, or sensor data with measurements, letting you enforce units at the validation boundary instead of hoping every caller remembered them. via PyCoder's Weekly newsletterUnit mix-ups have literally crashed spacecraft; now your Pydantic models can refuse them at the door.Annotate a field as Annotated[Quantity, PydanticPintQuantity('km')] and inputs like 12 meters arrive auto-converted to kilometersValidation covers string, numeric, and quantity inputs, and model_dump_json serializes quantities as readable unit stringsInstallable from PyPI as pydantic-pint, MIT licensed, with docs at pydantic-pint.readthedocs.ioEarly-stage solo project at version 0.4, so API stability and maintenance are open questions worth discussing Calvin #3: How Libraries Run Rust Inside Python (With PyO3) Pydantic v2's validation core (pydantic-core) is Rust under the hood, built with PyO3 — this post shows how that bridge actually works via a small hand-built JSON parserFour steps to get Rust into Python: write a normal Rust module, annotate with PyO3 macros (#[pyfunction], #[pymodule]), compile/install with maturin, then just import itThe parser builds a Rust tree first — Python never touches it until the boundary crossingKey insight: converting the Rust result into Python objects (.into_pyobject) is often the expensive part, not the parsing — 100,000 JSON values means ~100,000 Python objects built after parsing's already doneErrors cross the boundary too: Rust's typed errors convert into real Python exceptions (ValueError, FileNotFoundError) via From/?, so callers get clean Python semanticsTakeaway for anyone porting Rust in: if you're returning a scalar, don't sweat it; if you're returning a big structure, profile the boundary — that's the real cost, not the algorithm Michael #4: AWS acquires DuckLabs Thank you Dylan McConnell. What does this mean for the DuckDB ecosystem? DuckDB is the open-source in-process analytical SQL engine. MIT licensed. The IP is not owned by any company - it's held by the nonprofit DuckDB Foundation, which was created when the team spun out of CWI Amsterdam. Peter Boncz, the CWI representative on the Foundation board, describes it as the entity that holds all IP of open-source DuckDB. DuckLabs (ducklabs.com) is the company, formerly branded DuckDB Labs. Founded a little over five years ago by Hannes Mühleisen and Mark Raasveldt to give the DuckDB team a stable long-term home, bootstrapped deliberately instead of taking VC, grown to 30+ people in Amsterdam, funded by support and feature-prioritization contracts. It employs the core devs. It does not own DuckDB. DuckLake is one of three projects DuckLabs builds, what they call the Duck Stack: DuckDB, DuckLake, and Quack. DuckLake is the lakehouse format that puts catalog metadata in a SQL database instead of in files on object storage. Quack is newer - an RPC-style protocol that turns DuckDB into a client-server system where both ends are DuckDB instances, slated to stabilize in DuckDB v2.0 in September 2026. MotherDuck is a separate Seattle company, Jordan Tigani's, selling serverless ...
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    33 mins
  • #495 Banned

    #495 Banned
    Sep 8 2026
    Topics covered in this episode: EuroPython 2026 videos are onlineThe State of Django 2026: Boring is so backhtmx 4.0.0 has been released🐍 Functionally ZenExtrasJokeWatch on YouTube Sponsored by us! Support our work through: Our courses at Talk PythonConsulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedInCalvin: Mastodon / BlueSky / X / LinkedInShow: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: EuroPython 2026 videos are online The EuroPython Society has published all 117 recordings from EuroPython 2026 on the official EuroPython Conference YouTube channel. The conference ran July 13-19 in Krakow, Poland and celebrated the conference series' 25th anniversary. The playlist covers keynotes, panels, lightning talks, and full talk recordings across Python core, web, DevOps, data/ML, embedded, and other tracks. If you missed EuroPython 2026 in Krakow, this is the complete free on-demand archive of one of the year's biggest European Python events.117 videos now live on the EuroPython Conference YouTube channel, last updated Aug 17, 2026.Michael’s personal watch list. Calvin #2: The State of Django 2026: Boring is so back State of Django 2026 (JetBrains/DSF survey, ~3,500 devs, 40+ countries) - "boring is so back": Django's core stays reliable while everything around it moves fastCore is stable: Postgres 76–79% for 5 years running, templates ~80%, 43% already on Django 6.0AI is routine now (only 10% use none) but workflow's unsettled - Claude Code leads at 35%, and 56% still just use it for chat, not autonomous editsTooling is consolidating: uv and Ruff both at 43% adoption, each replacing several older single-purpose toolsType hints are winning (57% use them) but the checker is up for grabs - IDE-built-in leads at 40%, Mypy 32%, with ty/Pyrefly emergingTwo Django communities coexist happily: 72% server-rendered templates vs. 53% API-only - and htmx adoption jumped from 5% to 34% in five years Michael #3: htmx 4.0.0 has been released After 8 months of work, the htmx team shipped 4.0.0, a rewrite that moves internals from XMLHttpRequest to fetch() while keeping the API almost identical to htmx 2. Three changes may need action: attribute inheritance is now explicit via an :inherited suffix, event names follow a htmx:phase:action pattern, and history no longer caches pages in localStorage. Additions include built-in morph swaps, the new hx-partial tag, and many core extensions. htmx 2 stays supported and remains latest on npm until early 2027. htmx is the go-to frontend layer for Python server-rendered apps (Flask, Django, FastAPI), and 4.0 is deliberately low-drama: nearly behavior-compatible, so teams can upgrade on their own schedule and pick up morph swaps and streaming extensions.Explicit inheritance is the biggest migration item: hx-confirm, hx-headers, hx-target and friends no longer cascade to children unless you append :inherited; hx-disinherit and hx-inherit are goneA CLI upgrade checker (npx htmx.org@4.0.0 upgrade-check) flags spots needing :inherited, renames like hx-disable to hx-ignore, removed attrs like hx-vars, and old event names in templates and JSEvents follow htmx:phase:action (htmx:beforeRequest becomes htmx:before:request); most error events collapse into htmx:error and htmx:xhr:* events are removed with XMLHttpRequestHistory no longer snapshots pages in localStorage; back navigation re-fetches and swaps into the body, fixing a chronic support headache, with a new hx-history-cache extension for sessionStorage cachingNew features: out-of-the-box morphing swaps, the [HTML_REMOVED] tag for multi-element updates, streaming over SSE/WebSockets/multipart, and hx-live, their Alpine-inspired DOM scripting solutionNo forced upgrade: 2.x stays latest on npm until early 2027 (4.0 remains next) and is supported indefinitely; the team even ships official LLM skill files for guidance and upgrading Calvin #4: 🐍 Functionally Zen Functionally Zen (Kyle Adams, Test Double) - riffs on "simple is better than complex" with 7 extra tenets for Python simplicityCore claims: idiomatic > non-idiomatic, data > functions, pure functions > impure functions > classesFavorite example: a medical-dosage calculator replaced with a plain lookup dict - no logic, no tests neededBig idea: keep a thin "impure shell" around a "pure core" (Gary Bernhardt's functional core / imperative shell) - push side effects (API calls, DB, files) to the edgesSide note: constructors that do I/O are "poison pills" - the side effect infects every class that depends on themPayoff: pure functions and no-mock tests are just easier to read and reason about than the alternative Extras Calvin: uv ships ...
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    28 mins
  • #494 Python Wrapture

    #494 Python Wrapture
    Sep 1 2026
    Topics covered in this episode: OpenAI's Python SDK has migrated to HTTPX2TMOG - Native Task Manager for macOS, Windows, and Linuxwrapture - one wrapper for mocking, tracing, and observabilitylinkedin2md: turn your LinkedIn export into 40+ Markdown filesExtrasJokeWatch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk PythonConsulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedInCalvin: Mastodon / BlueSky / X / LinkedInShow: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: OpenAI's Python SDK has migrated to HTTPX2 The OpenAI Python SDK has migrated to HTTPX2, the Pydantic-stewarded fork of httpx. Pydantic picked it up citing "limited activity recently" in the original project, promising "a reliably maintained path forward."If you just use the default client, nothing to do. No code changes.The catch is TLS. Quoting the guide: HTTPX "previously verified certificates against the CA bundle provided by certifi. HTTPX2 instead uses the operating-system trust store, and the SDK no longer installs certifi."That "can break certificate verification in minimal container images without system CA certificates, environments using corporate TLS-inspecting proxies, and deployments that relied on a custom or modified certifi bundle."The fix is SSL_CERT_FILE or SSL_CERT_DIR, or pass your own ssl.SSLContext via verify.Deeper integrations need real edits: custom clients, auth handlers, hooks, and request mocking all take HTTPX2 objects now, and plain httpx is no longer pulled in transitively. So import httpx in your own code means declaring it yourself or moving over.Temporary escape hatch: a legacy HTTPX client Michael #2: TMOG - Native Task Manager for macOS, Windows, and Linux A native, deeply instrumented system monitor for macOS, Windows, and Linux, now in public beta - from Plummers' Software, i.e. Dave Plummer, who wrote the original Windows Task Manager and donated it to Microsoft in 1995. Wikipedia Three real native apps: Swift/AppKit on macOS, Win32 on Windows, C++/Qt 6 on Linux, with a shared C++ core keeping metric semantics aligned - no browser shell anywhere.One dense summary: CPU, clocks, thermals, GPU, memory, storage, network, energy, and the processes responsible for the load, all click-through.Per-core honesty: logical processor and NUMA views, P and E cores color-coded, optional kernel time, 60 FPS live meters.Memory with context: pressure, wired, compressed, cached, committed, available, and swap, plus configurable scrolling history.Processes that act like processes: tree view, filtering, sorting, follow mode, and native verbs including service and launchd control.Phosphor themes: light, dark, green, amber, blue, or mono, with color and saturation you tune yourself. Calvin #3: wrapture - one wrapper for mocking, tracing, and observability Graham Dumpleton, author of wrapt and the original New Relic Python agent, has released wrapture. The name is wrapt plus capture. The core idea: wrap real code instead of replacing it, so the real code still runs while you watch every call.Name a method with wrapture.binding(Class, "method"), open a timeline(), and you get a tape of what actually happened. Real return values, real nesting, arguments normalised against real signatures. tape.tree() prints the call graph as it ran.One mechanism, three jobs: monkey patching with a real lifecycle (apply, remove, suspend, plus returns, raises, transforms_args), unit testing that asserts on real call flow instead of a flat MagicMock call list, and ad-hoc tracing of a running app.The testing pitch is error paths. Inject TimeoutError at the payment gateway, then assert the ledger was never written. Stubs and mocks are strict and spec-required, and there is deliberately no bare Mock().Tracing needs no code at all. A wrapture.toml naming targets and a sink, run with python -m wrapture main.py, and you get a live call tree with timings. It captures ordinary logging calls as nested events, and with the otel extra it exports spans, metrics and correlated logs with W3C trace ids that join across services.Every line of code and docs was AI-written under their direction, and they say so up front. Two weeks from first commit, eleventh alpha, over 1000 tests, 150+ pages of docs. Alpha on PyPI, needs Python 3.12+ and wrapt 2.4.0+. Michael #4: linkedin2md: turn your LinkedIn export into 40+ Markdown files Via Juan Manuel Daza - a Python CLI that unpacks LinkedIn's data-export ZIP into clean, per-category Markdown you can drop straight into an LLM. One command: linkedin2md Complete_LinkedInDataExport.zip, plus o for output dir, -lang en|es, and -pdf.40+ output files: profile,...
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    29 mins
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