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Pylint configuration

What is Pylint?

Getting started with lsp-mode for Python

Early notes on using the new python-lsp-server (pylsp) in GNU Emacs

Getting Started with Python Poetry

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.

Using named arguments is nice for languages that support it, but this is not always a possibility. Even in Python, where time.sleep is defined with a single argument named secs, we can’t call sleep(secs=300) due to implementation reasons. In that case, we can give the value a name instead.Instead of this:time.sleep(300)Do this:sleep_seconds = 300 time.sleep(sleep_seconds)Now the code is unambiguous, and readable without having to consult the documentation.

for debugging purposes, a good combination is --lf --trace which would start a debug session with pdb at the beginning of the last test that failed:

pytest -l

If you start pytest with --pdb, it will start a pdb debugging session right after an exception is raised in your test. Most of the time this is not particularly useful as you might want to inspect each line of code before the raised exception.

pytest --lf

pytest -x

pytest --collect-only

pytest test_validator.py::test_regular_email_validates

Apart from shared fixtures you could place external hooks and plugins or modifiers for the PATH used by pytest to discover tests and implementation code.

pytest can read its project-specific configuration from one of these files: pytest.ini tox.ini setup.cfg

To have the fixture actually be used by one of your test, you simply add the fixture’s name as an argument

But the problem with Poetry is arguably down to the way Docker’s build works: Dockerfiles are essentially glorified shell scripts, and the build system semantic units are files and complete command runs. There is no way in a normal Docker build to access the actually relevant semantic information: in a better build system, you’d only re-install the changed dependencies, not reinstall all dependencies anytime the list changed. Hopefully someday a better build system will eventually replace the Docker default. Until then, it’s square pegs into round holes.

Third, you can use poetry-dynamic-versioning, a plug-in for Poetry that uses Git tags instead of pyproject.toml to set your application’s version. That way you won’t have to edit pyproject.toml to update the version. This seems appealing until you realize you now need to copy .git into your Docker build, which has its own downsides, like larger images unless you’re using multi-stage builds.

But if you’re doing some sort of continuous deployment process where you’re continuously updating the version field, your Docker builds are going to be slow.

VCR.py works primarily via the @vcr decorator. You can import this decorator by writing: import vcr.

The VCR.py library records the responses from HTTP requests made within your unit tests. The first time you run your tests using VCR.py is like any previous run. But the after VCR.py has had the chance to run once and record, all subsequent tests are:Fast! No more waiting for slow HTTP requests and responses in your tests.Deterministic. Every test is repeatable since they run off of previously recorded responses.Offline-capable! Every test can now run offline.

每月最多只能免费处理 1TB 的数据。如果需要更多则必须每月至少支付 49 美元。1TB/月对于测试工具和个人项目可能绰绰有余，但如果你需要它来实际公司使用，肯定是要付费的。

PyCaret是Python中的一个开源、低代码机器学习库，旨在减少从数据处理到模型部署的周期时间。

An extension to python markdown that takes metadata embedded as YAML in a page of markdown and render it as JSON-LD in the HTML created by MkDocs.

The metadata that we use for OCX is a profile of schema.org / LRMI,  OERSchema and few bits that we have added because we couldn’t find them elsewhere. Here’s what (mostly) schema.org metadata looks like in YAML:

"@context":
    - "http://schema.org"
    - "oer": "http://oerschema.org/"
    - "ocx": "https://github.com/K12OCX/k12ocx-specs/"
"@id": "#Lesson1"
"@type":
    - oer:Lesson
    - CreativeWork
learningResourceType: LessonPlan
hasPart:
  "@id": "#activity1-1"
author:
    "@type": Person
    name: Phil Barker

I’ve been experimenting with ways of putting JSON-LD schema.org metadata into HTML created by MkDocs. The result is a python-markdown plugin that will (hopefully) find blocks of YAML in markdown and insert then into the HTML that is generated.

So you could use this same technique with Python, Ruby, Lua, or other runtimes, too.

How to copy files?

Python | sep parameter in print()Difficulty Level : EasyLast Updated : 21 Jan, 2021The separator between the arguments to print() function in Python is space by default (softspace feature) , which can be modified and can be made to any character, integer or string as per our choice. The ‘sep’ parameter is used to achieve the same, it is found only in python 3.x or later. It is also used for formatting the output strings.

A best practice among Python developers is to avoid installing packages into a global interpreter environment. You instead use a project-specific virtual environment that contains a copy of a global interpreter. Once you activate that environment, any packages you then install are isolated from other environments. Such isolation reduces many complications that can arise from conflicting package versions. To create a virtual environment and install the required packages, enter the following commands as appropriate for your operating system:

How To Become A Task Automation Hero Using Python [Guide With Examples]

sudo pip3 install papis

Instead of “I have a type, it’s called MyType, it has a constructor, in the constructor I assign the property ‘A’ to the parameter ‘A’ (and so on)”, you say “I have a type, it’s called MyType, it has an attribute called a”

attrs lets you declare the fields on your class, along with lots of potentially interesting metadata about them, and then get that metadata back out.

>>> Point3D(1, 2, 3) == Point3D(1, 2, 3)

    def __eq__(self, other):
        if not isinstance(other, self.__class__):
            return NotImplemented
        return (self.x, self.y, self.z) == (other.x, other.y, other.z)
    def __lt__(self, other):
        if not isinstance(other, self.__class__):
            return NotImplemented
        return (self.x, self.y, self.z) < (other.x, other.y, other.z)

>>> Point3D(1, 2, 3)

def __repr__(self):
    return (self.__class__.__name__ +
        ("(x={}, y={}, z={})".format(self.x, self.y, self.z)))

Look, no inheritance! By using a class decorator, Point3D remains a Plain Old Python Class (albeit with some helpful double-underscore methods tacked on, as we’ll see momentarily).

class Point3D(object):
    def __init__(self, x, y, z):
        self.x = x
        self.y = y
        self.z = z

import attr
@attr.s
class Point3D(object):
    x = attr.ib()
    y = attr.ib()
    z = attr.ib()

Python

一个短视频APP视频内容安全审核的思路调研及实现汇总

基于nude(裸露程度)的色情图片识别 nudepy 这个库基本上可以视为上述方法的威力加强版 库内通过c语言实现了一个皮肤分类器,并基于较复杂的裸露程度来判别图片是否是色情图片 说明程序入口见pic_classify_nude.py,这里主要是对于nude库的封装 >>>from pic_classify_nude import test >>> >>>test('1.png') # 判断色情图片T/F True

100 000+ datapoints). This library solves this by downsampling the signal for the currently selected time window and then plotting the downsampled points.

I’d probably choose the official Docker Python image (python:3.9-slim-bullseye) just to ensure the latest bugfixes are always available.

So which should you use? If you’re a RedHat shop, you’ll want to use their image. If you want the absolute latest bugfix version of Python, or a wide variety of versions, the official Docker Python image is your best bet. If you care about performance, Debian 11 or Ubuntu 20.04 will give you one of the fastest builds of Python; Ubuntu does better on point releases, but will have slightly larger images (see above). The difference is at most 10% though, and many applications are not bottlenecked on Python performance.

There are three major operating systems that roughly meet the above criteria: Debian “Bullseye” 11, Ubuntu 20.04 LTS, and RedHat Enterprise Linux 8.

If we call this using Bash, it never gets further than the exec line, and when called using Python it will print lol as that's the only effective Python statement in that file.

#!/bin/bash
"exec" "python" "myscript.py" "$@"
print("lol")

For Python the variable assignment is just a var with a weird string, for Bash it gets executed and we store the result.

x() is the same as doing x.__call__()

How do you even begin to check if you can try and “call” a function, class, and whatnot? The answer is actually quite simple: You just see if the object implements the __call__ special method.

Python is referred to as a “duck-typed” language. What it means is that instead of caring about the exact class an object comes from, Python code generally tends to check instead if the object can satisfy certain behaviours that we are looking for.

everything is stored inside dictionaries. And the vars method exposes the variables stored inside objects and classes.

>>> page = Page.objects.get(title="A Blog post") >>> page <Page: A Blog post> # Note: the blog post is an instance of Page so we cannot access body, date or feed_image >>> page.specific <BlogPage: A Blog post>

Use settings to change the default templates used for each tag Specify templates using template and sub_menu_template arguments for any of the included menu tags (See Specifying menu templates using template tag parameters). Put your templates in a preferred location within your project and wagtailmenus will pick them up automatically (See Using preferred paths and names for your templates).

While main menus always have to be defined for each site, for flat menus, you can support multiple sites using any of the following approaches: Define a new menu for each site Define a menu for your default site and reuse it for the others Create new menus for some sites, but use the default site’s menu for others You can even use different approaches for different flat menus in the same project. If you’d like to learn more, take a look at the fall_back_to_default_site_menus option in Supported arguments

Have you noticed how the aricle pages are not shown below the ‘Latest news’ item, despite specifying allow_subnav=True on the menu item? Only pages with a show_in_menus value of True will be displayed (at any level) in rendered menus. The field is added by Wagtail, so is present for all custom page types. For page types that are better suited to showing on listing/index pages (for example: news articles or events) - you can set the show_in_menus_default attribute on the page type class to False to exclude them from menus by default.

indent=True here is treated as indent=1, so it works, but I’m pretty sure nobody would intend that to mean an indent of 1 space

bool is actually not a primitive data type — it’s actually a subclass of int!

complex is a supertype of float, which, in turn, is a supertype of int.

Now since the “compiling to bytecode” step above takes a noticeable amount of time when you import a module, Python stores (marshalls) the bytecode into a .pyc file, and stores it in a folder called __pycache__. The __cached__ parameter of the imported module then points to this .pyc file.When the same module is imported again at a later time, Python checks if a .pyc version of the module exists, and then directly imports the already-compiled version instead, saving a bunch of time and computation.

Bytecode is a set of micro-instructions for Python’s virtual machine. This “virtual machine” is where Python’s interpreter logic resides. It essentially emulates a very simple stack-based computer on your machine, in order to execute the Python code written by you.

Python is compiled. In fact, all Python code is compiled, but not to machine code — to bytecode

Python always runs in debug mode by default.The other mode that Python can run in, is “optimized mode”. To run python in “optimized mode”, you can invoke it by passing the -O flag. And all it does, is prevents assert statements from doing anything (at least so far), which in all honesty, isn’t really useful at all.

np = __import__('numpy') # Same as doing 'import numpy as np'

This refers to the module spec. It contains metadata such as the module name, what kind of module it is, as well as how it was created and loaded.

let’s say you only want to support integer addition with this class, and not floats. This is where you’d use NotImplemented

class MyNumber:
    def __add__(self, other):
        if isinstance(other, float):
            return NotImplemented

        return other + 42

__radd__ operator, which adds support for right-addition

class MyNumber:
    def __add__(self, other):
        return other + 42

    def __radd__(self, other):
        return other + 42

Now I should mention that all objects in Python can add support for all Python operators, such as +, -, +=, etc., by defining special methods inside their class, such as __add__ for +, __iadd__ for +=, and so on.

class MyNumber:
    def __add__(self, other):
        return other + 42

>>> num = MyNumber()
>>> num + 3
45

NotImplemented is used inside a class’ operator definitions, when you want to tell Python that a certain operator isn’t defined for this class.

Doing that would even catch KeyboardInterrupt, which would make you unable to close your program by pressing Ctrl+C.

every exception is a subclass of BaseException, and nearly all of them are subclasses of Exception, other than a few that aren’t supposed to be normally caught.

print(dir(__builtins__))

builtin scope in Python:It’s the scope where essentially all of Python’s top level functions are defined, such as len, range and print.When a variable is not found in the local, enclosing or global scope, Python looks for it in the builtins.

Global scope (or module scope) simply refers to the scope where all the module’s top-level variables, functions and classes are defined.

you can use the nonlocal keyword in Python to tell the interpreter that you don’t mean to define a new variable in the local scope, but you want to modify the one in the enclosing scope.

The enclosing scope (or nonlocal scope) refers to the scope of the classes or functions inside which the current function/class lives.

The local scope refers to the scope that comes with the current function or class you are in.

A builtin in Python is everything that lives in the builtins module.

in Python 3.0 (alongside 2.6), A new method was added to the str data type: str.format. Not only was it more obvious in what it was doing, it added a bunch of new features, like dynamic data types, center alignment, index-based formatting, and specifying padding characters.

machine learning libraries in Python

TypedDict is a dictionary whose keys are always string, and values are of the specified type. At runtime, it behaves exactly like a normal dictionary.

you should only use reveal_type to debug your code, and remove it when you’re done debugging.

What this says is “function double takes an argument n which is an int, and the function returns an int.

This tells mypy that nums should be a list of integers (List[int]), and that average returns a float.

from typing import List

def average(nums: List[int]) -> float:

for starters, use mypy --strict filename.py

If no explicit epoch is given, the implicit epoch is 0.

Finding how to check if a list is empty in Python is not so a tricky task as you think. There are few effective methods available to make your functionalities easy. And of course, list play a paramount role in python that come up with few tempting characteristics listed in the below for your reference.

You probably shouldn't use Alpine for Python projects, instead use the slim Docker image versions.

Before we dive into the details, here's a brief summary of the most important changes:

Python Examples