The other option is to run x86_64 Docker images on your ARM64 Mac machine, using emulation. Docker is packaged with software that will translate or emulate x86_64 machine code into ARM64 machine code on the fly; it’s slow, but the code will run.

Third, you can pre-compile wheels, store them somewhere, and install those directly instead of downloading the packages from PyPI.

If you have a compiler installed in your Docker image and any required native libraries and development headers, you can compile a native package from the source code. Basically, you add a RUN apt-get upgrade && apt-get install -y gcc and iterate until the package compiles successfully.

First, it’s possible the relevant wheels are available in newer versions of the libraries.

When you pip install murmurhash==1.0.6 on a M1/M2 Mac inside Docker, again it looks at the available files

When you pip install filprofiler==2022.05.0 on a M1/M2 Mac inside Docker, pip will look at the available files for that version, and then

In either case, pure Python will Just Work, because it’s interpreted at runtime: there’s no CPU-specific machine code, it’s just text that the Python interpreter knows how to run. The problems start when we start using compiled Python extensions. These are machine code, and therefore you need a version that is specific to your particular CPU instruction set.

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Here's my opinion, having written many thousands of lines of mypy code.

Overall, the code was significantly shorter compared to the tkinter version I did last year. That version had a few more features, but I'd say Textual felt much easier to reason about.

Or more directly

In Python, everything is an object – integers, strings, lists, functions, even classes themselves.

Try this code at app startup:

The trigger is when you allocate 700 or more container objects (classes, dicts, tuples, lists, etc) more than have been cleaned up, a GC cycle runs.

To summarize the three options we’ve seen, as well as a streaming ijson-based solution:

Always start with functionsGrow to classes once you feel you can group different subsets of functions

First of all, in Python there are no such things as "files" and I noticed this is the main source of confusion for beginners.If you're inside a directory that contains any __init__.py it's a directory composed of modules, not files.

the fact that the Poetry developers intentionally introduced failures to people’s CI/CD pipelines to motivate them to move away from Poetry’s legacy installer… Though we didn’t rely on the installer in our pipelines, this was the death knell for the continued use of Poetry.

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Mixin是什么概念?

有没有内容类似于《Python源码剖析》，但内容更新过，针对新版本的Python书籍？

There are plenty of articles about the emergence of PyScript for embedding Python code directly into HTML, but until now the creation of browser extensions in Python has been something of a closed door.

notice that defaultdict not only returns the default value, but also assigns it to the key that wasn't there before:

we might need a dictionary subclass, and then we need to access a key that does not exist in that dictionary

The table also includes links to the documentation of the dunder method under the emoji 🔗. When available, relevant Pydon'ts are linked under the emoji 🗒️.

>>> 3 in my_list False >>> my_list.__contains__(3) False

“dunder” comes from “double underscore”

dunder methods are methods that allow instances of a class to interact with the built-in functions and operators

I'm afraid you missed the joke ;-) While you believe spaces are required on both sides of an em dash, there is no consensus on this point. For example, most (but not all) American authorities say /no/ spaces should be used. That's the joke. In writing a line about "only one way to do it", I used a device (em dash) for which at least two ways to do it (with spaces, without spaces) are commonly used, neither of which is obvious -- and deliberately picked a third way just to rub it in. This will never change ;-)

This text has a line which has an ortographical typo in it. Please look at this line of text from the Zen of Python: There should be one-- and preferably only one --obvious way to do it.

Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one– and preferably only one –obvious way to do it.[a] Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than right now.[b] If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea – let's do more of those!

This motto has been very much discussed in the Perl community, and eventually extended to There’s more than one way to do it, but sometimes consistency is not a bad thing either (TIMTOWTDIBSCINABTE, pronounced Tim Toady Bicarbonate).[1] In contrast, part of the Zen of Python is, "There should be one— and preferably only one —obvious way to do it."

Python is known for using more memory than more optimized languages and, in this case, it uses 7 times more than PostgresML.

PostgresML outperforms traditional Python microservices by a factor of 8 in local tests and by a factor of 40 on AWS EC2.

You may need pathname2url

SSL is disabled by default in jdbc, npgsql, node-postgres, and pgx.

Mamba installs these packages in only a third of the time that Conda does. Much of that is due to less CPU usage, but even network downloads seem to be little faster; Mamba uses parallel downloads to speed them up.

So which should you use, pip or Conda? For general Python computing, pip and PyPI are usually fine, and the surrounding tooling tends to be better. For data science or scientific computing, however, Conda’s ability to package third-party libraries, and the centralized infrastructure provided by Conda-Forge, means setup of complex packages will often be easier.

Errors detected during execution are called exceptions and are not unconditionally fatal: you will soon learn how to handle them in Python programs.

There are (at least) two distinguishable kinds of errors: syntax errors and exceptions.

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# "func" called 3 times result = [func(x), func(x)**2, func(x)**3] # Reuse result of "func" without splitting the code into multiple lines result = [y := func(x), y**2, y**3]

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Write Better Python with Hypothesis

Every beginner-level tutorial for scientists should state during the first five minutes that you cannot expect stability and that you should either use Python only for throw-away code or else

What is not OK is what I perceive as the dominant attitude today: sell SciPy as a great easy-to-use tool for all scientists, and then, when people get bitten by breaking changes, tell them that it’s their fault for not having a solid maintenance plan for their code.

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when you use python -m pip with python being the specific interpreter you want to use, all of the above ambiguity is gone. If I say python3.8 -m pip then I know pip will be using and installing for my Python 3.8 interpreter (same goes for if I had said python3.7).

And if you're on Windows there is an added benefit to using python -m pip as it lets pip update itself. Basically because pip.exe is considered running when you do pip install --upgrade pip, Windows won't let you overwrite pip.exe. But if you do python -m pip install --upgrade pip you avoid that issue as it's python.exe that's running, not pip.exe.

It’s time to say goodbye to distutils package and switch to setuptools.

as soon as you switch to Python 3.11, you should get into habit of using import tomllib instead of import tomli

It's fine to use print if you're debugging an issue locally, but for any production-ready program that will run without user intervention, proper logging is a must.

Finally, if you don’t use either namedtuple nor dataclasses you might want to consider going straight to Pydantic.

You might be wondering why would you need to replace namedtuple? So, these are some reasons why you should consider switching to dataclasses

Using zoneinfo however has one caveat - it assumes that there's time zone data available on the system, which is the case on UNIX systems. If your system doesn't have timezone data though, then you should use tzdata package which is a first-party library maintained by the CPython core developers, which contains IANA time zone database.

Until Python 3.9, there wasn’t builtin library for timezone manipulation, so everyone was using pytz, but now we have zoneinfo in standard library, so it's time to switch!

As per docs, random module should not be used for security purposes. You should use either secrets or os.urandom, but the secrets module is definitely preferable, considering that it's newer and includes some utility/convenience methods for hexadecimal tokens as well as URL safe tokens.

pathlib has however many advantages over old os.path - while os module represents paths in raw string format, pathlib uses object-oriented style, which makes it more readable and natural to write

If you need to store duplicates, go for List or Tuple.For List vs. Tuple, if you do not intend to mutate, go for Tuple.If you do not need to store duplicates, always go for Set or Dictionary. Hash maps are significantly faster when it comes to determining if an object is present in the Set (e.g. x in set_or_dict).

O nome teve a sua origem no grupo humorístico britânico Monty Python, criador do programa Monty Pythons Flying Circus, embora muitas pessoas façam associação com o réptil do mesmo nome.

@app.get("/items/{item_id}") def read_item(item_id: int, q: Union[str, None] = None): return {"item_id": item_id, "q": q}

Python 3.11 is up to 10-60% faster than Python 3.10. On average, we measured a 1.25x speedup on the standard benchmark suite. See Faster CPython for details.

__init__.py is required to import the directory as a package, and should be empty.

Pyenv works by adding a special directory called shims in front of your PATH environment variable

If you are on Linux, you can simply download it from GitHub but the most convenient way is to use the pyenv-installer that is a simple script that will install it automatically on your distro, whatever it is, in the easiest possible way.

list(word_freq.items())

{k: v for k, v in sorted(x.items(), key=lambda item: item[1])}

for c in ax.containers: # set the bar label ax.bar_label(c, fmt='%.0f', label_type='edge', padding=1)

Without accounting for what we install or add inside, the base python:3.8.6-buster weighs 882MB vs 113MB for the slim version. Of course it's at the expense of many tools such as build toolchains3 but you probably don't need them in your production image.4 Your ops teams should be happier with these lighter images: less attack surface, less code that can break, less transfer time, less disk space used, ... And our Dockerfile is still readable so it should be easy to maintain.

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In the previous version, using the standard library, once the data is loaded we no longer to keep the file open. With this API the file has to stay open because the JSON parser is reading from the file on demand, as we iterate over the records.

One common solution is streaming parsing, aka lazy parsing, iterative parsing, or chunked processing.

Then, if the string can be represented as ASCII, only one byte of memory is used per character. If the string uses more extended characters, it might end up using as many as 4 bytes per character. We can see how much memory an object needs using sys.getsizeof()

wik2dict is a tool written in Python that converts MediaWiki SQL dumps into the DICT format. The script is available under the GNU General Public License. It is also capable of downloading Wikipedia, Wiktionary, Wikiquote, Wikinews and Wikibooks SQL dumps.