In essence, this article is about:

1) Training a sample model and uploading it to an S3 bucket:

```python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression import joblib

Load the Iris dataset

iris = load_iris() X, y = iris.data, iris.target

Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Train the logistic regression model

model = LogisticRegression(max_iter=200) model.fit(X_train, y_train)

Save the trained model to a file

joblib.dump(model, 'model.pkl') ```

1. Creating a sample Zappa config, because AWS Lambda doesn’t natively support Flask, we need to use Zappa, a tool that helps deploy WSGI applications (like Flask) to AWS Lambda:

```json { "dev": { "app_function": "app.app", "exclude": [ "boto3", "dateutil", "botocore", "s3transfer", "concurrent" ], "profile_name": null, "project_name": "flask-test-app", "runtime": "python3.10", "s3_bucket": "zappa-31096o41b" },

"production": {
    "app_function": "app.app",
    "exclude": [
        "boto3",
        "dateutil",
        "botocore",
        "s3transfer",
        "concurrent"
    ],
    "profile_name": null,
    "project_name": "flask-test-app",
    "runtime": "python3.10",
    "s3_bucket": "zappa-31096o41b"
}

} ```

1. Writing a sample Flask app:

```python import boto3 import joblib import os

Initialize the Flask app

app = Flask(name)

S3 client to download the model

s3 = boto3.client('s3')

Download the model from S3 when the app starts

s3.download_file('your-s3-bucket-name', 'model.pkl', '/tmp/model.pkl') model = joblib.load('/tmp/model.pkl')

@app.route('/predict', methods=['POST']) def predict(): # Get the data from the POST request data = request.get_json(force=True)

# Convert the data into a numpy array
input_data = np.array(data['input']).reshape(1, -1)

# Make a prediction using the model
prediction = model.predict(input_data)

# Return the prediction as a JSON response
return jsonify({'prediction': int(prediction[0])})

if name == 'main': app.run(debug=True) ```

1. Deploying this app to production (to AWS):

bash zappa deploy production

and later eventually updating it:

bash zappa update production

1. We should get a URL like this:

https://xyz123.execute-api.us-east-1.amazonaws.com/production

which we can query:

curl -X POST -H "Content-Type: application/json" -d '{"input": [5.1, 3.5, 1.4, 0.2]}' https://xyz123.execute-api.us-east-1.amazonaws.com/production/predict

You may use uv in production, but there may be still some undiscovered quirks.

About uv Python packaging tool

One of the best python compilers if you're a new dev and dont want to install Python locally.

CPython

cPython 是个什么？

• openai use LiveKit to deliver realtime voice
• playground: https://cloud.livekit.io/projects/

special methods = magic methods = dunder methods

Opening URL using Python's webbrowser module

6 differen debugging tools

See (above this annotation) the most optimized & CI friendly Python Docker build with Poetry (until this issue gets resolved)

Check this blog to find the key difference between Perl vs Python

This web page offers a brief overview of the append() function for lists in python.

This is an interesting aspect. Tuples are not limited to a single data type.

They went through 310 role descriptions and, even though role descriptions may vary significantly, they found 3 core skills that a large percentage of MLOps roles required:

📦 Docker and Kubernetes 🐍 Python 🌥 Cloud

very interesting

Popularity of Python package managers in 2024

연오

Why is using "src" folder to contain module files ambiguous? In this case "sample" seams more ambiguous cause it could be refering a folder containing sample data. Also how do you know the module is not called docs?

The time.thread_time() reports the time that the current thread has been executing.

The time begins or is zero when the current thread is first created.

Return the value (in fractional seconds) of the sum of the system and user CPU time of the current thread.

It is an equivalent value to the time.process_time(), except calculated at the scope of the current thread, not the current process.

This value is calculated as the sum of the system time and the user time.

thread time = user time + system time

The reported time does not include sleep time.

This means if the thread is blocked by a call to time.sleep() or perhaps is suspended by the operating system, then this time is not included in the reported time. This is called a “thread-wide” or “thread-specific” time.

The time.process_time() reports the time that the current process has been executed.

The time begins or is zero when the current process is first created.

Calculated as the sum of the system time and the user time:

process time = user time + system time

System time is time that the CPU is spent executing system calls for the kernel (e.g. the operating system)

User time is time spent by the CPU executing calls in the program (e.g. your code).

When a program loops through an array, it is accumulating user CPU time. Conversely, when a program executes a system call such as exec or fork, it is accumulating system CPU time.

The reported time does not include sleep time.

This means if the process is blocked by a call to time.sleep() or perhaps is suspended by the operating system, then this time is not included in the reported time. This is called a “process-wide” time.

As such, it only reports the time that the current process was executed since it was created by the operating system.

The time.monotonic() function returns time stamps from a clock that cannot go backwards, as its name suggests.

In mathematics, monotonic, e.g. a monotonic function means a function whose output over increases (or decreaes).

This means that the result from the time.monotonic() function will never be before the result from a prior call.

Return the value (in fractional seconds) of a monotonic clock, i.e. a clock that cannot go backwards.

It is a high-resolution time stamp, although is not relative to epoch-like time.time(). Instead, like time.perf_counter() uses a separate timer separate from the system clock.

The time.monotonic() has a lower resolution than the time.perf_counter() function.

This means that values from the time.monotonic() function can be compared to each other, relatively, but not to the system clock.

Like the time.perf_counter() function, time.monotonic() function is “system-wide”, meaning that it is not affected by changes to the system clock, such as updates or clock adjustments due to time synchronization.

Like the time.perf_counter() function, the time.monotonic() function was introduced in Python version 3.3 with the intent of addressing the limitations of the time.time() function tied to the system clock, such as use in short-duration benchmarking.

Monotonic clock (cannot go backward), not affected by system clock updates.

The time.perf_counter() function reports the value of a performance counter on the system.

It does not report the time since epoch like time.time().

Return the value (in fractional seconds) of a performance counter, i.e. a clock with the highest available resolution to measure a short duration. It does include time elapsed during sleep and is system-wide.

The returned value in seconds with fractional components (e.g. milliseconds and nanoseconds), provides a high-resolution timestamp.

Calculating the difference between two timestamps from the time.perf_counter() allows high-resolution execution time benchmarking, e.g. in the millisecond and nanosecond range.

The timestamp from the time.perf_counter() function is consistent, meaning that two durations can be compared relative to each other in a meaningful way.

The time.perf_counter() function was introduced in Python version 3.3 with the intended use for short-duration benchmarking.

The perf_counter() function was specifically designed to overcome the limitations of other time functions to ensure that the result is consistent across platforms and monotonic (always increasing).

For accuracy, the timeit module uses the time.perf_counter() internally.

The time.time() function reports the number of seconds since the epoch (epoch is January 1st 1970, which is used on Unix systems and beyond as an arbitrary fixed time in the past) as a floating point number.

The result is a floating point value, potentially offering fractions of a seconds (e.g. milliseconds), if the platforms support it.

The time.time() function is not perfect.

It is possible for a subsequent call to time.time() to return a value in seconds less than the previous value, due to rounding.

Note: even though the time is always returned as a floating point number, not all systems provide time with a better precision than 1 second. While this function normally returns non-decreasing values, it can return a lower value than a previous call if the system clock has been set back between the two calls.

1. Use time.time()
2. Use time.perf_counter()
3. Use time.monotonic()
4. Use time.process_time()
5. Use time.thread_time()

Note, each function returns a time in seconds and has an equivalent function that returns the time in nanoseconds, e.g. time.time_ns(), time.perf_counter_ns(), time.monotonic_ns(), time.process_time_ns() and time.thread_time_ns().

Recall that there are 1,000 nanoseconds in one microsecond, 1,000 microseconds in 1 millisecond, and 1,000 milliseconds in one second. This highlights that the nanosecond versions of the function are for measuring very short time scales indeed.

They are:

1. Trying to run coroutines by calling them.
2. Not letting coroutines run in the event loop.
3. Using the asyncio low-level API.
4. Exiting the main coroutine too early.
5. Assuming race conditions and deadlocks are impossible.

This is because you need to use the "async" keyword in order to use the "await" syntax, and the "await" syntax is the only way to actually run other async functions.`

A thread is a way of allowing your computer to break up a single process/program into many lightweight pieces that execute in parallel. Somewhat confusingly, Python's standard implementation of threading limits threads to only being able to execute one at a time due to something called the Global Interpreter Lock (GIL). The GIL is necessary because CPython's (Python's default implementation) memory management is not thread-safe. Because of this limitation, threading in Python is concurrent, but not parallel. To get around this, Python has a separate multiprocessing module not limited by the GIL that spins up separate processes, enabling parallel execution of your code. Using the multiprocessing module is nearly identical to using the threading module.

Asynchronous nature of threading: as one function waits, another one begins, and so on.

Reducing the performance hit of copying data between processes:

Option #1: Just use threads

Processes have overhead, threads do not. And while it’s true that generic Python code won’t parallelize well when using multiple threads, that’s not necessarily true for your Python code. For example, NumPy releases the GIL for many of its operations, which means you can use multiple CPU cores even with threads.

``` # numpy_gil.py import numpy as np from time import time from multiprocessing.pool import ThreadPool

arr = np.ones((1024, 1024, 1024))

start = time() for i in range(10): arr.sum() print("Sequential:", time() - start)

expected = arr.sum()

start = time() with ThreadPool(4) as pool: result = pool.map(np.sum, [arr] * 10) assert result == [expected] * 10 print("4 threads:", time() - start) ```

When run, we see that NumPy uses multiple cores just fine when using threads, at least for this operation:

$ python numpy_gil.py Sequential: 4.253053188323975 4 threads: 1.3854241371154785

Pandas is built on NumPy, so many numeric operations will likely release the GIL as well. However, anything involving strings, or Python objects in general, will not. So another approach is to use a library like Polars which is designed from the ground-up for parallelism, to the point where you don’t have to think about it at all, it has an internal thread pool.

Option #2: Live with it

If you’re stuck with using processes, you might just decide to live with the overhead of pickling. In particular, if you minimize how much data gets passed and forth between processes, and the computation in each process is significant enough, the cost of copying and serializing data might not significantly impact your program’s runtime. Spending a few seconds on pickling doesn’t really matter if your subsequent computation takes 10 minutes.

Option #3: Write the data to disk

Instead of passing data directly, you can write the data to disk, and then pass the path to this file: * to the subprocess (as an argument) * to parent process (as the return value of the function running in the worker process).

The recipient process can then parse the file.

``` import pandas as pd import multiprocessing as mp from pathlib import Path from tempfile import mkdtemp from time import time

def noop(df: pd.DataFrame): # real code would process the dataframe here pass

def noop_from_path(path: Path): df = pd.read_parquet(path, engine="fastparquet") # real code would process the dataframe here pass

def main(): df = pd.DataFrame({"column": list(range(10_000_000))})

with mp.get_context("spawn").Pool(1) as pool:
    # Pass the DataFrame to the worker process
    # directly, via pickling:
    start = time()
    pool.apply(noop, (df,))
    print("Pickling-based:", time() - start)

    # Write the DataFrame to a file, pass the path to
    # the file to the worker process:
    start = time()
    path = Path(mkdtemp()) / "temp.parquet"
    df.to_parquet(
        path,
        engine="fastparquet",
        # Run faster by skipping compression:
        compression="uncompressed",
    )
    pool.apply(noop_from_path, (path,))
    print("Parquet-based:", time() - start)

if name == "main": main() `` **Option #4:multiprocessing.shared_memory`**

Because processes sometimes do want to share memory, operating systems typically provide facilities for explicitly creating shared memory between processes. Python wraps this facilities in the multiprocessing.shared_memory module.

However, unlike threads, where the same memory address space allows trivially sharing Python objects, in this case you’re mostly limited to sharing arrays. And as we’ve seen, NumPy releases the GIL for expensive operations, which means you can just use threads, which is much simpler. Still, in case you ever need it, it’s worth knowing this module exists.

Note: The module also includes ShareableList, which is a bit like a Python list but limited to int, float, bool, small str and bytes, and None. But this doesn’t help you cheaply share an arbitrary Python object.

A bad option for Linux: the "fork" context

You may have noticed we did multiprocessing.get_context("spawn").Pool() to create a process pool. This is because Python has multiple implementations of multiprocessing on some OSes. "spawn" is the only option on Windows, the only non-broken option on macOS, and available on Linux. When using "spawn", a completely new process is created, so you always have to copy data across.

On Linux, the default is "fork": the new child process has a complete copy of the memory of the parent process at the time of the child process’ creation. This means any objects in the parent (arrays, giant dicts, whatever) that were created before the child process was created, and were stored somewhere helpful like a module, are accessible to the child. Which means you don’t need to pickle/unpickle to access them.

Sounds useful, right? There’s only one problem: the "fork" context is super-broken, which is why it will stop being the default in Python 3.14.

Consider the following program:

``` import threading import sys from multiprocessing import Process

def thread1(): for i in range(1000): print("hello", file=sys.stderr)

threading.Thread(target=thread1).start()

def foo(): pass

Process(target=foo).start() ```

On my computer, this program consistently deadlocks: it freezes and never exits. Any time you have threads in the parent process, the "fork" context can cause in potential deadlocks, or even corrupted memory, in the child process.

You might think that you’re fine because you don’t start any threads. But many Python libraries start a thread pool on import, for example NumPy. If you’re using NumPy, Pandas, or any other library that depends on NumPy, you are running a threaded program, and therefore at risk of deadlocks, segfaults, or data corruption when using the "fork" multiprocessing context. For more details see this article on why multiprocessing’s default is broken on Linux.

You’re just shooting yourself in the foot if you take this approach.

To enable this, when you pass Python objects between processes using Python’s multiprocessing library:

• On the sender side, the arguments get serialized to bytes with the pickle module.
• On the receiver side, the bytes are unserialized using pickle.

This serialization and deserialization process involves computation, which can potentially be slow.

Multiple threads let you run code in parallel, potentially on multiple CPUs. On Python, however, the global interpreter lock makes this parallelism harder to achieve.

Multiple processes also let you run code in parallel—so what’s the difference between threads and processes?

All the threads inside a single process share the same memory address space. If thread 1 in a process stores some memory at address 0x7f0cd1a88810, thread 2 can access the same memory at the same address. That means passing objects between threads is cheap: you just need to get the pointer to the memory address from one thread to the other. A memory address is 8 bytes: this is not a lot of data to move around.

In contrast, processes do not share the same memory space. There are some shared memory facilities provided by the operating system, typically, and we’ll get to that later. But by default, no memory is shared. That means you can’t just share the address of your data across processes: you have to copy the data.

How do you load only a subset of the rows?

When you load your data, you can specify a skiprows function that will randomly decide whether to load that row or not:

```

from random import random

def sample(row_number): ... if row_number == 0: ... # Never drop the row with column names: ... return False ... # random() returns uniform numbers between 0 and 1: ... return random() > 0.001 ... sampled = pd.read_csv("/tmp/voting.csv", skiprows=sample) len(sampled) 973 ```

If parts of your data don’t impact your analysis, no need to waste memory keeping extraneous details around.

Interesting: using ternary operator in except clause

a thread pool and imap_unordered():

``` pool = multiprocessing.dummy.Pool(2)

for result in pool.imap_unordered(do_stuff, things_to_do): print(result) ```

Here, concurrency is limited by the fixed number of threads.

Gunicorn forks a base process into n worker processes, and each worker is managed by Uvicorn (with the asynchronous uvloop). Which means:

• Each worker is concurrent
• The worker pool implements parallelism

This way, we can have the best of both worlds: concurrency (multithreading) and parallelism (multiprocessing).

Using the asyncio:

``` import asyncio

from fastapi import FastAPI

app = FastAPI()

@app.get("/asyncwait") async def asyncwait(): duration = 0.05 await asyncio.sleep(duration) return {"duration": duration} ```

There will be moments when you don't have to await for every single task to be processed right away.

We do this by using asyncio.as_completed which returns a generator with completed coroutines.

Async only makes sense if you're doing IO.

There's ZERO benefit in using async to stuff like this that is CPU-bound:

``` import asyncio

async def sum_two_numbers_async(n1: int, n2: int) -> int: return n1 + n2

async def main(): await sum_two_numbers_async(2, 2) await sum_two_numbers_async(4, 4)

asyncio.run(main()) ```

Your code might even get slower by doing that due to the Event Loop.

That's because Python async only optimizes IDLE time!

Creating a task triggers the async operation, and it needs to be awaited at some point.

For example:

task = create_task(my_async_function('arg1')) result = await task

As we're creating many tasks, we need asyncio.gather which awaits all tasks to be done.

as I explain in another article.

Fast API is a high-level web framework like flask, but that happens to be async, unlike flask. With the added benefit of using type hints and pydantic to generate schemas.

It's not a building block like twisted, gevent, trio or asyncio. In fact, it's built on top of asyncio. It's in the same group as flask, bottle, django, pyramid, etc. Although it's a micro-framework, so it's focused on routing, data validation and API delivery.

``` import re import time

import httpx import trio

urls = [ "https://www.bitecode.dev/p/relieving-your-python-packaging-pain", "https://www.bitecode.dev/p/hype-cycles", "https://www.bitecode.dev/p/why-not-tell-people-to-simply-use", "https://www.bitecode.dev/p/nobody-ever-paid-me-for-code", "https://www.bitecode.dev/p/python-cocktail-mix-a-context-manager", "https://www.bitecode.dev/p/the-costly-mistake-so-many-makes", "https://www.bitecode.dev/p/the-weirdest-python-keyword", ]

title_pattern = re.compile(r"<title[^>]>(.?)</title>", re.IGNORECASE)

user_agent = ( "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/116.0" )

async def fetch_url(url): start_time = time.time()

async with httpx.AsyncClient() as client:
    headers = {"User-Agent": user_agent}
    response = await client.get(url, headers=headers)
    match = title_pattern.search(response.text)
    title = match.group(1) if match else "Unknown"
    print(f"URL: {url}\nTitle: {title}")

end_time = time.time()
elapsed_time = end_time - start_time
print(f"Time taken for {url}: {elapsed_time:.4f} seconds\n")

async def main(): global_start_time = time.time()

# That's the biggest API difference
async with trio.open_nursery() as nursery:
    for url in urls:
        nursery.start_soon(fetch_url, url)

global_end_time = time.time()
global_elapsed_time = global_end_time - global_start_time
print(f"Total time taken for all URLs: {global_elapsed_time:.4f} seconds")

if name == "main": trio.run(main) ```

Because it doesn't create nor schedule coroutines immediately (notice the nursery.start_soon(fetch_url, url) is not nursery.start_soon(fetch_url(url))), it will also consume less memory. But the most important part is the nursery:

# That's the biggest API difference async with trio.open_nursery() as nursery: for url in urls: nursery.start_soon(fetch_url, url)

The with block scopes all the tasks, meaning everything that is started inside that context manager is guaranteed to be finished (or terminated) when it exits. First, the API is better than expecting the user to wait manually like with asyncio.gather: you cannot start concurrent coroutines without a clear scope in trio, it doesn't rely on the coder's discipline. But under the hood, the design is also different. The whole bunch of coroutines you group and start can be canceled easily, because trio always knows where things begin and end.

As soon as things get complicated, code with curio-like design become radically simpler than ones with asyncio-like design.

For many years, the very talented dev and speaker David Beazley has been showing unease with asyncio's design, and made more and more experiments and public talks about what could an alternative look like. It culminated with the excellent Die Threads presentation, live coding the sum of the experience of all those ideas, that eventually would become the curio library. Watch it. It’s so good.

Trio is not compatible with asyncio, nor gevent or twisted by default. This means it's also its little own async island.

But in exchange for that, it provides a very different internal take on how to deal with this kind of concurrency, where every coroutine is tied to an explicit scope, everything can be awaited easily, or canceled.

``` import re import time

import gevent from gevent import monkey

monkey.patch_all() # THIS MUST BE DONE BEFORE IMPORTING URLLIB

from urllib.request import Request, urlopen

urls = [ "https://www.bitecode.dev/p/relieving-your-python-packaging-pain", "https://www.bitecode.dev/p/hype-cycles", "https://www.bitecode.dev/p/why-not-tell-people-to-simply-use", "https://www.bitecode.dev/p/nobody-ever-paid-me-for-code", "https://www.bitecode.dev/p/python-cocktail-mix-a-context-manager", "https://www.bitecode.dev/p/the-costly-mistake-so-many-makes", "https://www.bitecode.dev/p/the-weirdest-python-keyword", ]

title_pattern = re.compile(r"<title[^>]>(.?)</title>", re.IGNORECASE)

user_agent = ( "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/116.0" )

We move the fetching into a function so we can isolate it into a green thread

def fetch_url(url): start_time = time.time()

headers = {"User-Agent": user_agent}

with urlopen(Request(url, headers=headers)) as response:
    html_content = response.read().decode("utf-8")
    match = title_pattern.search(html_content)
    title = match.group(1) if match else "Unknown"

    print(f"URL: {url}\nTitle: {title}")

end_time = time.time()
elapsed_time = end_time - start_time

print(f"Time taken: {elapsed_time:.4f} seconds\n")

def main(): global_start_time = time.time()

# Here is where we convert synchronous calls into async ones
greenlets = [gevent.spawn(fetch_url, url) for url in urls]
gevent.joinall(greenlets)

global_end_time = time.time()
global_elapsed_time = global_end_time - global_start_time

print(f"Total time taken: {global_elapsed_time:.4f} seconds")

main() ```

No async, no await. No special lib except for gevent. In fact it would work with the requests lib just as well. Very few modifications are needed, for a net perf gain.

The only danger is if you call gevent.monkey.patch_all() too late. You get a cryptic error that crashes your program.

• if name == "main" is important for multiprocessing because it will spawn a new Python, that will import the module. You don't want this module to spawn a new Python that imports the module that will spawn a new Python...
• If the function to submit to the executor has complicated arguments to be passed to it, use a lambda or functools.partial.
• max_worker = 1 is a very nice way to get a poor man’s task queue.

• When you don't need to share data between tasks.
• When you are CPU bound.
• When you don't have too many tasks to run at the same time.
• When you need true parallelism and want to exercise your juicy cores.

• Tasks (network, file, etc.) that needs less than 10_000 I/O interactions per second. The number is higher than you would expect, because threads are surprisingly cheap nowadays, and you can spawn a lot of them without bloating memory too much. The limit is more the price of context switching. This is not a scientific number, it's a general direction that you should challenge by measuring your own particular case.
• When you need to share data between the tasks.
• When you are not CPU bound.
• When you are OK to execute tasks a bit slower to you ensure you are not blocking any of them (E.G: user UI and a long calculation).
• When you are CPU bound, but the CPU calculations are delegating to a C extension that releases the GIL, such as numpy. Free parallelism on the cheap, yeah!

E.G: a web scraper, a GUI to zip files, a development server, sending emails without blocking web page rendering, etc.

Pretty much the same, but, we use ProcessPoolExecutor instead.

```python from concurrent.futures import ProcessPoolExecutor, as_completed

...

with ProcessPoolExecutor(max_workers=5) as executor: ... ```

Note that here the number of workers maps to the number of CPU cores I want to dedicate to the program. Processes are way more expensive than threads, as each starts a new Python instance.

```python from concurrent.futures import ThreadPoolExecutor, as_completed

def main(): with ThreadPoolExecutor(max_workers=len(URLs)) as executor: tasks = {} for url in URLs: future = executor.submit(fetch_url, url) tasks[future] = url

    for future in as_completed(tasks):
        title = future.result()
        url = tasks[future]
        print(f"URL: {url}\nTitle: {title}")

```

```python from concurrent.futures import ThreadPoolExecutor, as_completed

with ThreadPoolExecutor(max_workers=5) as executor: executor.submit(do_something_blockint) ```

$ python3 -m compileall .

This will place the compiled bytecode of all Python files in the current directory in pycache/ and show you any compiler errors.

The CPython interpreter really is an interpreter. But it also is a compiler. Python must go through a few stages before ever running the first line of code:

1. scanning
2. parsing

Older versions of Python added an additional stage:

1. scanning
2. parsing
3. checking for valid assignment targets

Let’s compare this to the stages of compiling a C program:

1. ~~preprocessing~~
2. lexical analysis (another term for “scanning”)
3. syntactic analysis (another term for “parsing”)
4. ~~semantic analysis~~
5. ~~linking~~

Here is the buggy program:

python 1 / 0 print() = None if False ñ = "hello

Each line of code generates a different error message:

• 1 / 0 will generate ZeroDivisionError: division by zero.
• print() = None will generate SyntaxError: cannot assign to function call.
• if False will generate SyntaxError: expected ':'.
• ñ = "hello will generate SyntaxError: EOL while scanning string literal.

The question is… which will be reported first?

Spoilers: the specific version of Python matters (more than I thought it would) so keep that in mind if you see different results.

The first error message detected is on the last line of source code. What this tells us is that Python must read the entire source code file before running the first line of code. If you have a definition in your head of an “interpreted language” that includes “interpreted languages run the code one line at a time”, then I want you to cross that out!

In this article you started implementing your own version of Python. To do so, you needed to create four main components:

A tokenizer: * accepts strings as input (supposedly, source code); * chunks the input into atomic pieces called tokens; * produces tokens regardless of their sequence making sense or not.

A parser: * accepts tokens as input; * consumes the tokens one at a time, while making sense they come in an order that makes sense; * produces a tree that represents the syntax of the original code.

A compiler: * accepts a tree as input; * traverses the tree to produce bytecode operations.

An interpreter: * accepts bytecode as input; * traverses the bytecode and performs the operation that each one represents; * uses a stack to help with the computations.

``` from .compiler import Bytecode, BytecodeType

...

class Interpreter: def init(self, bytecode: list[Bytecode]) -> None: self.stack = Stack() self.bytecode = bytecode self.ptr: int = 0

def interpret(self) -> None:
    for bc in self.bytecode:
        # Interpret this bytecode operator.
        if bc.type == BytecodeType.PUSH:
            self.stack.push(bc.value)
        elif bc.type == BytecodeType.BINOP:
            right = self.stack.pop()
            left = self.stack.pop()
            if bc.value == "+":
                result = left + right
            elif bc.value == "-":
                result = left - right
            else:
                raise RuntimeError(f"Unknown operator {bc.value}.")
            self.stack.push(result)

    print("Done!")
    print(self.stack)

```

It's an abstract syntax tree because it is a tree representation that doesn't care about the original syntax we used to write the operation. It only cares about the operations we are going to perform.

The tokenizer is the part of your program that accepts the source code and produces a linear sequence of tokens – bits of source code that you identify as being relevant.

• Tokenizer takes source code as input and produces tokens;
• Parser takes tokens as input and produces an AST;
• Compiler takes an AST as input and produces bytecode;
• Interpreter takes bytecode as input and produces program results.

``` import _xxsubinterpreters as interpreters import _xxinterpchannels as channels

interp_id = interpreters.create(site=site) channel_id = channels.create()

interpreters.run_string( interp_id, """ import _xxinterpchannels as channels channels.send('hello!') """, shared={ "channel_id": channel_id } )

print(channels.recv(channel_id)) ```

``` import _xxsubinterpreters as interpreters

interp_id = interpreters.create(site=site)

interpreters.run_string( interp_id, "print(message)", shared={ "message": "hello world!" } )

interpreters.run_string( interp_id, """ for message in messages: print(message) """, shared={ "messages": ("hello world!", "this", "is", "me") } )

interpreters.destroy(interp_id) ```

``` import _xxsubinterpreters as interpreters

interp_id = interpreters.create(site=site)

interpreters.run_string(interp_id, "print('hello world')") interpreters.run_string(interp_id, "print('hello universe')")

interpreters.destroy(interp_id) ```

``` from threading import Thread import _xxsubinterpreters as interpreters

t = Thread(target=interpreters.run, args=("print('hello world')",)) t.start() ```

``` import _xxsubinterpreters as interpreters

interpreters.run(''' print("Hello World") ''') ```

Whether using sub interpreters or multiprocessing you cannot simply send existing Python objects to worker processes.

Multiprocessing uses pickle by default. When you start a process or use a process pool, you can use pipes, queues and shared memory as mechanisms to sending data to/from the workers and the main process. These mechanisms revolve around pickling. Pickling is the builtin serialization library for Python that can convert most Python objects into a byte string and back into a Python object.

Pickle is very flexible. You can serialize a lot of different types of Python objects (but not all) and Python objects can even define a method for how they can be serialized. It also handles nested objects and properties. However, with that flexibility comes a performance hit. Pickle is slow. So if you have a worker model that relies upon continuous inter-worker communication of complex pickled data you’ll likely see a bottleneck.

Sub interpreters can accept pickled data. They also have a second mechanism called shared data. Shared data is a high-speed shared memory space that interpreters can write to and share data with other interpreters. It supports only immutable types, those are:

• Strings
• Byte Strings
• Integers and Floats
• Boolean and None
• Tuples (and tuples of tuples)

To share data with an interpreter, you can either set it as initialization data or you can send it through a channel.

Once you get over the hurdle of starting one, this quickly becomes the most important point. You have two questions to answer:

• How do we communicate between workers?
• How do we manage the state of workers?

For example, you can import something in your module and reference it from inside the thread function:

```python import threading from super.duper.module import cool_function

def worker(info): # This already exists in the interpreter state cool_function()

info = {'a': 1} thread = Thread(target=worker, args=(info, )) ```

The Python standard library has a few options for concurrent programming, depending on some factors:

• Is the task you’re completing IO-bound (e.g. reading from a network, writing to disk)
• Does the task require CPU-heavy work, e.g. computation
• Can the tasks be broken into small chunks or are they large pieces of work?

Here are the models:

• Threads are fast to create, you can share any Python objects between them and have a small overhead. Their drawback is that Python threads are bound to the GIL of the process, so if the workload is CPU-intensive then you won’t see any performance gains. Threading is very useful for background, polling tasks like a function that waits and listens for a message on a queue.
• Coroutines are extremely fast to create, you can share any Python objects between them and have a miniscule overhead. Coroutines are ideal for IO-based activity that has an underlying API that supports async/await.
• Multiprocessing is a Python wrapper that creates Python processes and links them together. These processes are slow to start, so the workload that you give them needs to be large enough to see the benefit of parallelising the workload. However, they are truly parallel since each one has it’s own GIL.
• Sub interpreters have the parallelism of multiprocessing, but with a much faster startup time.

Tested it on my side, and poetry install of one Python project took 44 seconds instead of 2 minutes 53 seconds, so it's nearly a 4x speed increase!

This is WRONG.

numpy.any() checks if there is at least one non-zero element in an array.

In simpler terms, Sutherland's thesis is discussing the basic operations of a data structure known as a ring. A ring is a type of list where the elements are connected in a circular manner. The operations he mentions are:

1. Inserting a new element into the ring at a specified location.
2. Removing an element from the ring.
3. Moving all elements of one ring, in order, into another ring at a specified location.
4. Performing an operation on each member of a ring in either forward or reverse order.

These operations are implemented using macro instructions in the compiler language. A macro instruction is a directive to the compiler which specifies how an input pattern should be mapped to an output pattern.

The thesis also discusses the generation of new elements. Subroutines are used to set up new elements in free spaces in the storage structure. When parts of the drawing are deleted, the registers representing them become free and are placed in a 'FREES' ring. New components are set up at the end of the storage area, while free blocks are allowed to accumulate. A process called 'garbage collection' periodically compacts the storage structure by removing the free blocks and relocating the information above them.

In Python, the ring data structure can be implemented using a doubly linked list. Here's a simple example:

```python class Node: def init(self, data): self.data = data self.next = None self.prev = None

class Ring: def init(self): self.head = None

def append(self, data):
    if not self.head:
        self.head = Node(data)
        self.head.next = self.head
        self.head.prev = self.head
    else:
        new_node = Node(data)
        new_node.prev = self.head.prev
        new_node.next = self.head
        self.head.prev.next = new_node
        self.head.prev = new_node

def display(self):
    temp = self.head
    while True:
        print(temp.data, end = " ")
        temp = temp.next
        if temp == self.head:
            break

```

In this example, the Node class represents an element in the ring, and the Ring class represents the ring itself. The append method is used to add a new element to the ring, and the display method is used to print all elements in the ring.

Ivan writes about a concept called "Ring Structure". This is a way of organizing and linking different elements or components in a system.

In simpler terms, imagine you have a bunch of different objects (like points and lines in a drawing program like Sketchpad). You want to keep track of how these objects are related to each other. For example, you might want to know all the lines that end at a particular point.

To do this, Ivan uses a "ring structure". Each object has a "string of pointers" - basically a list of references to other objects. This list is circular - the last item in the list points back to the first item. This makes it easy to move forwards and backwards through the list.

Each object has two "registers" or slots for keeping track of these relationships. One slot is for the object itself, and the other is for the list of related objects.

Ivan uses the terms "hen" and "chicken" to describe these slots. The "hen" is the object itself, and the "chicken" is the list of related objects.

Here's a simple Python code example to illustrate this concept:

```python class Point: def init(self): self.hen = self self.chickens = []

class Line: def init(self, point1, point2): self.hen = self self.chickens = [point1, point2] point1.chickens.append(self) point2.chickens.append(self) ```

In this example, a Point object has a hen that refers to itself and a list of chickens that will contain any Line objects that end at this point. When a Line is created, it adds itself to the chickens list of its end points.

The "ring structure" is a way to organize and link different elements in a system, making it easier to find and update related elements.

Mnemonics for Registers: Instead of remembering numerical indices, we use human-readable keys.

python point = {'TYPE': 'Point', 'PVAL_X': 5, 'PVAL_Y': 10}

Flexibility: If we want to change the internal structure, we can easily do so by changing the keys.

```python

Changing 'PVAL_X' to 'X_COORD'

point = {'TYPE': 'Point', 'X_COORD': 5, 'PVAL_Y': 10} ```

Conventions:

• The first component ('TYPE') indicates the type of element.
• Numerical information like coordinates is stored at the end.

python line = {'TYPE': 'Line', 'START': 'point1', 'END': 'point2', 'LENGTH': 7.2}

Pointers and Topology: We can use pointers (references) to other elements to establish relationships.

python point1 = {'TYPE': 'Point', 'PVAL_X': 1, 'PVAL_Y': 1} point2 = {'TYPE': 'Point', 'PVAL_X': 4, 'PVAL_Y': 5} line = {'TYPE': 'Line', 'START': point1, 'END': point2}

Relocation: If we move point1 to a new variable, we update the pointer in line.

python new_point1 = point1 # Relocating point1 to new_point1 line['START'] = new_point1 # Updating the pointer

Segregation of Data: Numerical data is at the end, so if we need to move elements, the numerical data remains untouched.

```python

Even if we relocate, the numerical data ('PVAL_X' and 'PVAL_Y') remains the same.

```

This section provides a step-by-step guide on setting up PyCharm for automated testing using the 'Pytest Web Framework'.

Use poetry init to create a sample pyproject.toml, and then trigger this line to export requirements.txt into a pyproject.toml

Slices follow a similar logic than indexing in NumPy array's. array[:2] selects a range of elements along a single axis,, but array[:2, 1:] does it along two axis.

A generator is a function-like iterator object.

Returning multiple values in Python is expressed as a tuple by default and each value is correspondingly assigned. Optionally, you can return a dictionary if specified.

Nested list comprehensions follow the same logic as nested for loops. The difference strives that in list comprehensions the filtered variable is mentioned twice.

So surprised that you can output a calendar view using Python

python -m site <--- see useful information about your Python installation

```python from flask import Flask, request from collections import defaultdict import re import random

GREEN ="🟩" YELLOW ="🟨" WHITE ="⬜"

def get_answers(): with open("allowed_answers.txt") as f: answers = set(l for l in f.read().splitlines() if l) return answers

def get_guesses(): guesses = get_answers() with open("allowed_guesses.txt") as f: for l in f.read().splitlines(): if l: guesses.add(l) return guesses

app = Flask(name, static_folder="static") app.answers = get_answers() app.guesses = get_guesses() word = random.choice(list(app.answers)) print(f"The word is {word}")

def with_header(content): return f"""

<html> <head> <link rel="search" type="application/opensearchdescription+xml" title="searchGame" href="http://searchgame:5000/static/opensearch.xml" /> </head> <body> {content} </body></html>

"""

@app.route("/") def home(): return with_header("

Right click on the address bar to install the search engine.

")

@app.route("/search") def search(): return with_header(f"Content: {request.args.get('q')}")

def to_result(guess, answer): chars = [WHITE] * 5 count = defaultdict(int) for idx, (g, a) in enumerate(zip(guess, answer)): if g == a: chars[idx] = GREEN else: count[a] += 1

for idx, g in enumerate(guess):
    if g in count and count[g] > 0 and chars[idx] == WHITE:
        chars[idx] = YELLOW
        count[g] -= 1

return "".join(chars)

def maybe_error(guess): if len(guess) < 5: return f"less than 5 characters" if len(guess) > 5: return f"greater than 5 characters" if guess not in app.guesses: return f"not in wordlist" return None

@app.route("/game") def game(): query = request.args.get("q") guesses = [x for x in re.split("[. ]", query) if x] response = [] if not guesses: response.append("Enter 5-letter guesses separated by spaces") else: most_recent = guesses[-1] # Don't show "too short" error for most recent guess if len(most_recent) < 5: guesses = guesses[:-1] if not guesses: response.append("Enter a wordle guess") for guess in guesses[::-1]: error = maybe_error(guess) if error is None: result = to_result(guess, word) s = f"{guess} | {result}" if result == GREEN * 5: s = f"{s} | CORRECT!" response.append(s) else: response.append(f"{guess} | ERROR: {error}")

return [query, response]

```

حالا می خواد بره با Axios وصلش کنه

حالا رفت سراغ Vue

در واقع تعیین می کنه که درخواست ها از چه پروتکلی، با چه IP یا Domain Name و رو چه Port می تونی جواب بدی. خیلی خووبه براش تعیین کنی تو فقط باید از این IP و Port که در Front تعیین کردی استفاده کنی

تازه رفت سراغ Flask-origin چقد جاالب.

اول یه Virtual Environment می سازه

Recommendation for when to use Ruff over PyLint or Flake8

```python def split_user(userid): """ Return the user and domain parts from the given user id as a dict.

For example if userid is u'acct:seanh@hypothes.is' then return
{'username': u'seanh', 'domain': u'hypothes.is'}'

:raises InvalidUserId: if the given userid isn't a valid userid

"""
match = re.match(r"^acct:([^@]+)@(.*)$", userid)
if match:
    return {"username": match.groups()[0], "domain": match.groups()[1]}
raise InvalidUserId(userid)

```

```html

<body> <div> {% for chat in chats %} <div>{{ chat.contents }}</div> <button id={{chat.id}} ✅ onClick=getId(id) ✅ > print this chat_id out </button> {% endfor %} </div> ... <script> function getId(id){ console.log(id) } </script> </body>

```

Frontend and backend examples of Python's build backends

setup.py is considered a "legacy" functionality these days

setuptools can now utilize pyproject.toml

PyPDF4 is compared with 6 other python libraries to manipulate, create and annotate pdf files via python

Dataclasses give you a lot more clarity of what the function returns, in comparison to returning tuples or dictionaries

3 ways of running jupyter notebook in a container

python 内置函数next()

we are all beginners

I think the requirements of this were not spelled out well. After reading this over a couple of times, I think the problem should be…

"Add, subtract, and compare binary numbers in Python as strings, without converting them to decimal."

I'll take on that problem sometime when I get free time!

It's as simple as adding "sys.stdout" to the "stream" attribute.

This is a pretty old article, from 2011. Note that it refers to Python 2.6 and 2.7. Today, Python is up to version 3.12.

NICE tables comparing build frameworks, front-ends & backends & others, discovered from this SO.

class starter

Experienced talk on python project configurations

One of the recommendations for Python multi-stage Docker builds. Thanks to pip install --user, the packages won't be spread across 3 different paths.