PyPA still advertises pipenv all over the place and only mentions poetry a couple of times, although poetry seems to be the more mature product.

The main selling point for Anaconda back then was that it provided pre-compiled binaries. This was especially useful for data-science related packages which depend on libatlas, -lapack, -openblas, etc. and need to be compiled for the target system.

Many of Python’s standard tools allow already for configuration in pyproject.toml so it seems this file will slowly replace the setup.cfg and probably setup.py and requirements.txt as well. But we’re not there yet.

Any import statement compiles to a series of bytecode instructions, one of which, called IMPORT_NAME, imports the module by calling the built-in __import__() function.

databases is an async SQL query builder that works on top of the SQLAlchemy Core expression language.

The fact that FastAPI does not come with a development server is both a positive and a negative in my opinion. On the one hand, it does take a bit more to serve up the app in development mode. On the other, this helps to conceptually separate the web framework from the web server, which is often a source of confusion for beginners when one moves from development to production with a web framework that does have a built-in development server (like Django or Flask).

FastAPI makes it easy to deliver routes asynchronously. As long as you don't have any blocking I/O calls in the handler, you can simply declare the handler as asynchronous by adding the async keyword like so:

Fixtures are created when first requested by a test, and are destroyed based on their scope: function: the default scope, the fixture is destroyed at the end of the test.

When pytest goes to run a test, it looks at the parameters in that test function’s signature, and then searches for fixtures that have the same names as those parameters. Once pytest finds them, it runs those fixtures, captures what they returned (if anything), and passes those objects into the test function as arguments.

“Fixtures”, in the literal sense, are each of the arrange steps and data. They’re everything that test needs to do its thing.

Here is how you can create a fully configured new project in a just a couple of minutes (assuming you have pyenv and poetry installed already).

After reading through PEP8, you may wonder if there is a way to automatically check and enforce these guidelines? Flake8 does exactly this, and a bit more. It works out of the box, and can be configured in case you want to change some specific settings.

Pylint is a very strict and nit-picky linter. Google uses it for their Python projects internally according to their guidelines. Because of it’s nature, you’ll probably spend a lot of time fighting or configuring it. Which is maybe not bad, by the way. Outcome of such strictness can be a safer code, however, as a consequence - longer development time.

The goal of this tutorial is to describe Python development ecosystem.

For Windows, there is pyenv for Windows - https://github.com/pyenv-win/pyenv-win. But you’d probably better off with Windows Subsystem for Linux (WSL), then installing it the Linux way.

There are often multiple versions of python interpreters and pip versions present. Using python -m pip install <library-name> instead of pip install <library-name> will ensure that the library gets installed into the default python interpreter.

In addition to SQLAlchemy core queries, you can also perform raw SQL queries

query = notes.insert()
values = [
    {"text": "example2", "completed": False},
    {"text": "example3", "completed": True},
]
await database.execute_many(query=query, values=values)

query = "INSERT INTO notes(text, completed) VALUES (:text, :completed)"
values = [
    {"text": "example2", "completed": False},
    {"text": "example3", "completed": True},
]
await database.execute_many(query=query, values=values)

query = notes.select()
rows = await database.fetch_all(query=query)

query = "SELECT * FROM notes WHERE completed = :completed"
rows = await database.fetch_all(query=query, values={"completed": True})

If you're serious about neural networks, I have one recommendation. Try to rebuild this network from memory.

Some options (you will have to use your own judgment, based on your use case)

Create an account or session

Uber and Booking.com’s ecosystem was originally JVM-based but they expanded to support Python models/scripts. Spotify made heavy use of Scala in the first iteration of their platform until they received feedback like:some ML engineers would never consider adding Scala to their Python-based workflow.

Integrated access to the pdb debugger and the Python profiler.

Python’s Significant Whitespace Problems

TensorFlow.js provides theLayers API,which mirrors the Keras API as closely as possible, in-cluding the serialization format.

Uses Pydantic which enforces type hints at runtime.

Advantages and Disadvantages of Python for Product Development

if a module's name has no dots, it is not considered to be part of a package. It doesn't matter where the file actually is on disk.

if you imported moduleX (note: imported, not directly executed), its name would be package.subpackage1.moduleX. If you imported moduleA, its name would be package.moduleA. However, if you directly run moduleX from the command line, its name will instead be __main__, and if you directly run moduleA from the command line, its name will be __main__. When a module is run as the top-level script, it loses its normal name and its name is instead __main__.

A file is loaded as the top-level script if you execute it directly, for instance by typing python myfile.py on the command line. It is loaded as a module if you do python -m myfile, or if it is loaded when an import statement is encountered inside some other file.

The majority of Python packaging tools also act as virtualenv managers to gain the ability to isolate project environments. But things get tricky when it comes to nested venvs: One installs the virtualenv manager using a venv encapsulated Python, and create more venvs using the tool which is based on an encapsulated Python. One day a minor release of Python is released and one has to check all those venvs and upgrade them if required. PEP 582, on the other hand, introduces a way to decouple the Python interpreter from project environments. It is a relative new proposal and there are not many tools supporting it (one that does is pyflow), but it is written with Rust and thus can't get much help from the big Python community. For the same reason it can't act as a PEP 517 backend.

However, the place where pip places that package might not be in your $PATH (thus requiring you to manually update your $PATH afterwards), and on windows the pip install might not take care of python-specific issues for you (see "Notes for Windows Users", above). As such, installation via package managers is recommended instead.

If your python3 executable is named "python" instead of "python3" (this particularly appears to affect a number of Windows users), then you'll also need to modify the first line of git-filter-repo to replace "python3" with "python".

you want to pass a function as an argument to higher-order functions

Lambda functions are used when you need a function for a short period of time.

Cookiecutter takes a source directory tree and copies it into your new project. It replaces all the names that it finds surrounded by templating tags {{ and }} with names that it finds in the file cookiecutter.json. That’s basically it. [1]

you can take the opportunity of using Python as a corresponding module pexpect is written for it (http://pexpect.sourceforge.net). It's clear that Python language should be installed on the system beforehand

Holy xxx, it takes 3.37s to calculate merely 1,000,000 reciprocal numbers. The same logic in C takes just a blink: 9ms; C# takes 19ms; Nodejs takes 26ms; Java takes 5ms! and Python takes self-doubting 3.37 SECONDS.

Python by the Numbers: How Strong Is the Demand for Python on the Software Development Market in 2021?

As to why both is_a? and kind_of? exist: I suppose it's part of Ruby's design philosophy. Python would say there should only be one way to do something; Ruby often has synonymous methods so you can use the one that sounds better. It's a matter of preference.

I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)

session.query(Book.author, Chapter.title).select_from(Book).join(Book.chapters) or session.query(Book).options(load_only("author"), joinedload("chapters").load_only("title"))

PyPy uses a technique known as meta-tracing, which transforms an interpreter into a tracing just-in-time compiler.

Default: 0

You can use container values, that wraps actual success or error value into a thin wrapper with utility methods to work with this value. That’s exactly why we have created @dry-python/returns project. So you can make your functions return something meaningful, typed, and safe.

I'm not a fan of listing exceptions functions can throw, especially here in Python, where it's easier to ask forgiveness than permission.

Here is a quick recap table of every technology we discussed in this blog post.

We recommend the Alpine image as it is tightly controlled and small in size (currently under 5 MB), while still being a full Linux distribution. This is fine advice for Go, but bad advice for Python, leading to slower builds, larger images, and obscure bugs.

When It Makes Sense to Use Python to Build an eCommerce Website

Did you know that everything you can do in VBA can also be done in Python? The Excel Object Model is what you use when writing VBA, but the same API is available in Python as well.See Python as a VBA Replacement from the PyXLL documentation for details of how this is possible.

You can write Excel worksheet functions in your Jupyter notebook too. This is a really great way of trying out ideas without leaving Excel to go to a Python IDE.

Use the magic function “%xl_get” to get the current Excel selection in Python. Have a table of data in Excel? Select the top left corner (or the whole range) and type “%xl_get” in your Jupyter notebook and voila! the Excel table is now a pandas DataFrame.

to run Python code in Excel you need the PyXLL add-in. The PyXLL add-in is what lets us integrate Python into Excel and use Python instead of VBA

How to Hire an In-house Python & Django Developer: Python/Django Interview Questions, Tips, and Advice

Python Development Trends

Turning my Jupyter-compatible Python code into Flask-compatible Python code took dozens of hours. I ran into small bugs and errors

Comparison between pytest and unittes test frameworks

The behaviour of the argument function is extended by the decorator without actually modifying it.

As of Python 3.6, f-strings are a great new way to format strings.

A location into which the result is stored.

Text Sequence Type

和 Python 里的字符串和列表切片不同，你不能在 start, stop 或者 step 这些参数中使用负数。:

line.strip()

# A recursive generator that generates Tree leaves in in-order.

Any function containing a yield keyword is a generator function;

Generators

if expression is creating a tuple, it must be surrounded with parentheses.

list comprehensions

An object is called iterable if you can get an iterator for it.

An iterator is an object representing a stream of data;

key 形参用来指定在进行比较前要在每个列表元素上调用的函数

Sprawdźmy który rodzaj modelu daje najlepszą skuteczność: Python sns.boxplot(data=models_df, x='score', y='model') 1 sns.boxplot(data=models_df, x='score', y='model')

Przy tych danych wygląda, że właściwie nie ma większej różnicy (nie bijemy się tutaj o 0.01 punktu procentowego poprawy accuracy modelu). Może więc czas treningu jest istotny? Python sns.boxplot(data=models_df, x='time_elapsed', y='model') 1 sns.boxplot(data=models_df, x='time_elapsed', y='model')

Teraz w zagnieżdżonych pętlach możemy sprawdzić każdy z każdym podmieniając klasyfikatory i transformatory (cała pętla trochę się kręci):

hyperscript is more concise because it's just a function call and doesn't require a closing tag. Using it will greatly simplify your tooling chain.

Used by convention to avoid naming conflicts with Python keywords.

meant as a hint to the programmer only.

The second is a “throwaway” variable that we don’t need just yet, denoted with an underscore.

If <key> is not found, it returns None

The len() function returns the number of key-value pairs in a dictionary

the in and not in operators

key values are simple strings

Restrictions on Dictionary Values

it is not quite correct to say an object must be immutable to be used as a dictionary key.

Restrictions on Dictionary Keys#

a dictionary key must be of a type that is immutable.

the values contained in the dictionary don’t need to be the same type.

Python does guarantee that the order of items in a dictionary is preserved.

they have nothing to do with the order of the items in the dictionary.

If you refer to a key that is not in the dictionary, Python raises an exception:

Dictionary elements are not accessed by numerical index

A list of tuples works well for this:

dd = dict([('a',1),('b',2)])

Dictionaries and lists share the following characteristics:

Dictionaries differ from lists primarily in how elements are accessed:

the instance behaves like a standard dictionary.

The first argument to the Python defaultdict type must be a callable that takes no arguments and returns a value.

Using the Python defaultdict Type for Handling Missing Keys

not to call it using the parentheses at initialization time.

the dictionary assigns it the default value that results from calling list().

if you call .setdefault() on an existing key, then the call won’t have any effect on the dictionary.

four available ways to handle missing keys

if you try to access or modify a missing key, then defaultdict will automatically create the key and generate a default value for it.

Decide when and why to use a Python defaultdict rather than a standard dict

augmented assignment operator

Sets are collections of unique objects,

for large datasets, it can also be a lot faster and more efficient.

Diving Deeper Into defaultdict

When and why to use a Python defaultdict rather than a regular dict

Note that __missing__() is not called for any operations besides __getitem__().

use code to parameterize calls:

python -c "
from mymodule import set_dragon_feeding_schedule, Creatures, Date
set_dragon_feeding_schedule(
    feeding_times=['10:00', '14:00', '18:00'],
    dishes={Creatures.Tiger: 2, Creatures.Human: 1},
    start_day=Date('1020-03-01'),
)
"

python -m mymodule \
    set_dragon_feeding_schedule \
    --feeding-times ['10:00','14:00','18:00'] # hopefully this way it gets recognized \
    # how will you define parsing a dict with enum to integer mapping? 
    --dishes=Creatures.Tiger:2 \
    --dishes=Creatures.Human:1 \
    --start-day=1020-03-21 # BTW bash allows no comments in multiline calls

That’s it. Types are parsed, checked and converted. Defaults and description are picked from function itself. Even provides bash completions you can install. You wrote no code for that!

import typer
from pathlib import Path

app = typer.Typer()

@app.command()
def find_dragon(name: str, path: Path, min_age_years: int = 200):
    <implementation goes here>

@app.command()
def feed_dragon(dragon_name: str, n_humans: int = 3):
    <implementation goes here>

if __name__ == "__main__":
    app()

python example.py find_dragon 'Drake' --path /on/my/planet

Merge (|) and update (|=) operators have been added to the built-in dict class. Those complement the existing dict.update and {**d1, **d2} methods of merging dictionaries.

Use Streamlit if you want to get going as quickly possible and don’t have strong opinions or many custom requirements.Use Dash if you need something more flexible and mature, and you don’t mind spending the extra engineering time.

Here’s a table showing the tradeoffs:

The Census FTP page contains the microdata and dictionaries identifying each variable name, location, value range, and whether it applies to a restricted sample. To follow this example, download the April 2017 compressed data file that matches your operating system and unpack it in the same location as the python code. Next download the January 2017 data dictionary text file and save it in the same location.

3.5 PEP 478 security 2015-09-13 2020-09-13 Larry Hastings

This command will give you the top 25 stocks that had the highest anomaly score in the last 14 bars of 60 minute candles.

List Comprehensions

Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.

TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

What Is Python & Django and Why Are They Considered a Top Choice for Web Development?

from fastapi import FastAPI app = FastAPI() @app.get("/") async def root(): return {"message": "Hello World"}

barChart = pygal.Bar(height=400)[barChart.add(x[0], x[1]) for x in mean_per_state.items()]display(HTML(base_html.format(rendered_chart=barChart.render(is_unicode=True))))

from IPython.display import display, HTMLbase_html = """<!DOCTYPE html><html> <head> <script type="text/javascript" src="http://kozea.github.com/pygal.js/javascripts/svg.jquery.js"></script> <script type="text/javascript" src="https://kozea.github.io/pygal.js/2.0.x/pygal-tooltips.min.js""></script> </head> <body> <figure> {rendered_chart} </figure> </body></html>"""

def main(name: str = typer.Argument("World", hidden=True)):

def main(name: str = typer.Argument(..., help="The name of the user to greet")): typer.echo(f"Hello {name}")

import random import typer def get_name(): return random.choice(["Deadpool", "Rick", "Morty", "Hiro"]) def main(name: str = typer.Argument(get_name)): typer.echo(f"Hello {name}") if __name__ == "__main__": typer.run(main)

def main(name: Optional[str] = typer.Argument(None)):

Libra is a machine learning API designed for non-technical users. This means that it assumes that you have no background in ML whatsoever.

Return sends a specified value back to its caller whereas Yield can produce a sequence of values. We should use yield when we want to iterate over a sequence, but don’t want to store the entire sequence in memory. Yield are used in Python generators. A generator function is defined like a normal function, but whenever it needs to generate a value, it does so with the yield keyword rather than return. If the body of a def contains yield, the function automatically becomes a generator function.

Why don't you allow a range without end, like (1..)? There are two advantages. First, we can write ary[1..] instead of ary[1..-1]. The -1 is one of the most I dislike in Ruby. It is very magical, ugly, redundant, and disappointing. I envy Python's ary[1:]. I know that ary.drop(1) is slightly fast, but too long for such a common operation, IMO. Second, we can write (1..).each {|n| ... }.

from difflib import SequenceMatcher squeeze=SequenceMatcher( None, A, B ) print "A - B = [%s]"%( reduce( lambda p,q: p+q, map( lambda t: squeeze.a[t[1]:t[2]], filter(lambda x:x[0]!='equal', squeeze.get_opcodes() ) ) ) )

The mixed functional, OO and procedural nature of python has always been, IMO, one of its strengths.

However, this is not working in Python 3, as print has changed from a command to a function, and reduce, filter and map have been declared unpythonic.

# scenario-1: delete in session: SA might set category_id of all chilren Products to None c1 = session.query(Category).get(1) session.delete(c1) session.commit() # scenario-2: delete without loading an object into the session: SA will perform no additional logic session.query(Category).filter(Category.id == 2).delete() session.commit()