Python Bytes

Sponsored by Datadog: pythonbytes.fm/datadog Special guest: Hannah Stepanek Watch on YouTube Michael #1: We Downloaded 10,000,000 Jupyter Notebooks From Github – This Is What We Learned by Alena Guzharina from JetBrains Used the hundreds of thousands of publicly accessible repos on GitHub to learn more about the current state of data science. I think it’s inspired by work showcased here on Talk Python. 2 years ago there were 1,230,000 Jupyter Notebooks published on GitHub. By October 2020 this number had grown 8 times, and we were able to download 9,720,000 notebooks. 8x growth. Despite the rapid growth in popularity of R and Julia in recent years, Python still remains the most commonly used language for writing code in Jupyter Notebooks by an enormous margin. Python 2 went from 53% → 11% in the last two years. Interesting graphs about package usage Not all notebooks are story telling with code: 50% of notebooks contain fewer than 4 Markdown cells and more than 66 code cells. Although there are some out

Sponsored by us! Support our work through: Our courses at Talk Python Training pytest book Patreon Supporters Special guest: Jennifer Stark - @_JAStark & guest on talkpython.fm/259 Watch on YouTube Brian #1: Do you really need a virtualenv? Frost Ming doesn’t think so, based on the article You don't really need a virtualenv The link slug is “introducing-pdm”, which I think would be a better title, but the first did work to get people to talk about it. Also, “Try PEP 582 today” may have been appropriate. Teaching new people is a problem: Telling them to first type python -m venv venv Then type source venv/bin/activate or . venv/bin/activate Unless you’re on windows, then type venv\scripts\activate.bat Then type pip install -r requirements.txt Yeah. It’s not pretty, not fun, and good luck not having anyone ask questions about why this is necessary. Also the Python version is specified in the venv. So if you upgrade Python versions, what happens to existing venvs? The article also discusses level

Sponsored by Datadog: pythonbytes.fm/datadog Special guest: Jeremy Tanner Watch on YouTube Brian #1: Constant Folding in Python Arpit Bhayani Constant Folding is when a language replaces constant expressions at compile time rather than computing them at runtime. CPython does this while creating the bytecode. We can use dis to see it in action >>> import dis >>> dis.dis("day_sec = 24 * 60 * 60") 1 0 LOAD_CONST 0 (86400) 2 STORE_NAME 0 (day_sec) 4 LOAD_CONST 1 (None) 6 RETURN_VALUE Python tries to fold lots of constants, but not all. Seems to be based on size >>> x = 2 ** 64 # folded >>> x = 4 ** 64 # not folded >>> a = "-" * 4096 # folded >>> a = "-" * 4097 # not folded Discussion continues with a discussion of how CPython folding is implemented recursively and elegantly. Key takeaway for me: Remember to n

Sponsored by Linode! pythonbytes.fm/linode Special guest: Ogi Moore Watch the live stream on YouTube. Michael #1: diskcache via Ian Maurer Python disk-backed cache (Django-compatible). Faster than Redis and Memcached. Pure-Python. The cloud-based computing of 2020 puts a premium on memory. Gigabytes of empty space is left on disks as processes vie for memory. Among these processes is Memcached (and sometimes Redis) which is used as a cache. Wouldn't it be nice to leverage empty disk space for caching? Features: Pure-Python Fully Documented Benchmark comparisons (alternatives, Django cache backends) 100% test coverage Hours of stress testing Performance matters Django compatible API Thread-safe and process-safe Supports multiple eviction policies (LRU and LFU included) Keys support "tag" metadata and eviction Developed on Python 3.8 Tested on CPython 3.5, 3.6, 3.7, 3.8 Tested on Linux, Mac OS X, and Windows Tested using Travis CI and AppVeyor CI Brian #2: TOML is 1.0.0 now. What does that mean for

Sponsored by Datadog: pythonbytes.fm/datadog Special guest: Jousef Murad, Engineered Mind podcast (audio, video) Watch on YouTube Brian #1: pip search. Just don’t. pip search [query] is supposed to “Search for PyPI packages whose name or summary contains [query]” The search feature looks like it’s going to be removed and the PyPI api for it removed. Alternative, and better approach, just manually look at pypi.org and search for stuff. Right now it does this: $ pip search pytest ERROR: Exception: Traceback (most recent call last): ... [longish traceback ommited] --- xmlrpc.client.Fault: [Fault -32500: "RuntimeError: PyPI's XMLRPC API has been temporarily disabled due to unmanageable load and will be deprecated in the near future. See https://status.python.org/ for more information."] The Python Infrastructure status page says, as of Jan 12: “Update - The XMLRPC Search endpoint remains disabled due to ongoing request volume. As of this update, there has been no reduction in inbou

Sponsored by us! Support our work through: Our courses at Talk Python Training pytest book Patreon Supporters Special guest: Jason McDonald Watch on YouTube Michael #1: 5 ways I use code as an astrophysicist Video by Dr. Becky (i.e. Dr Becky Smethurst, an astrophysicist at the University of Oxford) She has a great YouTube channel to check out. #1: Image Processing (of galaxies from telescopes) Noise removal #2: Data analysis Image features (brightness, etc) One example: 600k “rows” of galaxy properties #3: Model fitting e.g. linear fit (visually as well through jupyter) e.g. Galaxies and their black holes grow in mass together Color of galaxies & relative star formation #4: Data visualization #5: Simulations Beautiful example of galaxies colliding Star meets black hole Brian #2: A Visual Intro to NumPy and Data Representation Jay Alammar I’ve started using numpy more frequently in my own work. Problem: I think of np.array like a Python list. But that’s not right. This visualization gu

2020 will be one for the history books, won't it? I've put together a great group to look back on 2020 - from the Python perspective. Join Brian and Michael along with Cecil Phillip, Ines Montani, Jay Miller, Paul Everitt, Reuven Lerner, and Matt Harrison for a light-hearted and fun look back on the major Python events of 2020. Video version of this episode: Watch on YouTube Guests Cecil Phillip Ines Montani Jay Miller Paul Everitt Reuven Lerner Matt Harrison

Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Special guest: Anthony Shaw Stream on YouTube as well Watch on YouTube Michael #1: Django Ledger Project by Miguel Sanda The mission is to provide free and open source accounting software that could easily replace commercial alternatives like QuickBooks. Django Ledger supports: Chart of Accounts. Financial Statements (Income Statement & Balance Sheets). Automatic financial ratio & insight calculations. Multi tenancy. Hierarchical entity management. Self-contained Ledgers, Journal Entries & Transactions. Financial Activities Support (operational/financial/investing). Basic OFX & QFX file import. Bills & Invoices with optional progressible functionality. Basic navigational templates. Entity administration & entity manager support. Bank Accounts. Not quite ready for production. This project is actively looking for contributors. Any financial and/or accounti

Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Special guest: Shari Eskenas Catch the video edition live stream on YouTube Watch on YouTube Be part of the episode by subscribing and “smashing that bell” over at pythonbytes.fm/youtube Brian #1: pytest 6.2 is out pytester fixture for plugin testing. Like testdir, but a better name, and uses pathlib.Path objects. verbose mode now shows the reason a test was skipped in the terminal line after the SKIPPED, XFAIL, or XPASS Can use monkeypatch as a context manager with .context() and it’s available both in test and fixture functions, but also in helper functions by using pytest.Monkeypatch.context(). import os from contextlib import contextmanager import pytest def test_foo_1(monkeypatch): with monkeypatch.context() as mp: mp.setenv("foo", "bar") assert os.getenv("foo") == "bar" @contextmanager def some_func(): w

Sponsored by Techmeme Ride Home podcast: pythonbytes.fm/ride Special guest: Matthew Feickert Live streamed on Youtube: youtube.com/watch?v=ug3OR-BQ7Hw Brian #1: Introducing FARM Stack - FastAPI, React, and MongoDB Aaron Basset Describes a todo CRUD application, available on github, to show the concepts in action. Animated gif showing how to use the FastAPI interactive documentation to understand the API in a browser. Shows How app connects to routes and endpoints. The run call with uvicorn to get an async even loop going Connecting to a MongoDB database Defining models and how easy it is to set up a schema. Discusses routes and endpoints and how to hook up all the CRUD endpoints. The React bit is an application that changes as you modify the elements through the endpoint interactive session. Very cool way to work and get something running fast. Michael #2: py-applescript How would you like to automate your mac with Python rather than the dreadful AppleScript? py-applescript is an easy-to-use Pytho

The live stream recording on YouTube. Special guest: Jay Miller Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Brian #1: Analyzing Kickstarter Campaigns with Python Data Science Tools Article title: “Kickstarter Projects — Do They Succeed?” Aditya Patkar Using a Kaggle dataset of 378,661-ish projects up to 2018. Looks at using pandas data frames to explore the data. Using .describe() data frame method to learn a lot. Uses matplotlib and seaborn to analyze the data further. Odd statement that I’m not sure is straight faced or a really dry joke: “The data from 1970 seems to be bad or insignificant data.” Examples of using heat maps, line graphs, bar charts, to look at different aspects. Some results: 35.64% of projects are successful (meaning goal hit) tech asks for the most for goals, and has the highest average per backer. Comics has the lowest pledged amount per backer average. Nice that you can use the techniques to ask yo

Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Michael #1: Running Python on .NET 5 by Anthony Shaw Talked about pyjion way back when on episode 49 with Brett Cannon. .NET 5 was released on November 10, 2020. It is the cross-platform and open-source replacement of the .NET Core project and the .NET project that ran exclusively on Windows since the late 90’s. See the conference about it if you want to go deeper. Performance: I just saw a SO post about someone complaining their Python was 31x slower than C#. The most common way around this performance barrier is to compile Python extensions from C or using something like Cython. .NET 5 CLR comes bundled with a performant JIT compiler (codenamed RyuJIT) that will compile .NETs IL into native machine instructions on Intel x86, x86-64, and ARM CPU architectures. Pyjion is a project to replace the core execution loop of CPython by transpiling CPython bytecode to ECMA CIL and then using

See the full show notes for this episode on the website at pythonbytes.fm/208.

See the full show notes for this episode on the website at pythonbytes.fm/207.

See the full show notes for this episode on the website at pythonbytes.fm/206.

See the full show notes for this episode on the website at pythonbytes.fm/205.

See the full show notes for this episode on the website at pythonbytes.fm/204.

See the full show notes for this episode on the website at pythonbytes.fm/203.

See the full show notes for this episode on the website at pythonbytes.fm/202.

See the full show notes for this episode on the website at pythonbytes.fm/201.