git VS CPython

Download Git Bash. This is a tool developers use to write Git commands on a PC. I use this on my computer. Just go to gitforwindows.org and click the download button to start downloading the file.

Go to the Git for Windows website.

At work when I work on Windows (currently stuck using Mac for the first time in my career at work), but on Windows I would use https://gitforwindows.org/ which also uses msys2 and that is a very popularly supported free product using msys2 on Windows. If you ever forget the URL, search for "git bash for windows".

Git installed and configured on your machine. For Windows, For Mac

How to install?? Before going further make sure you have installed GIT BASH because these commands will not work on windows terminal. Before installing foundry we need to install latest version of the rust. To install rust run below command in your bash terminal

Install gitbash 😸

I use windows, mac and linux basically interchangeably for coding. For windows, I install the Git Bash package [https://gitforwindows.org/] and then just use chocolatey as a package manager for stuff like python and node.js. Bash gives me a completely functional commandline with my C drive mounted as `/c`, and I can write code using VSCode. I don't really have issues. With this setup, it operates indistinguishably from the other two platforms.

For me, the key really is the commandline. Learning powershell is not something I'm going to do.

Mac (OSX) • Windows • Linux

For more information on the Git for Windows project, which is separate from Git itself, you can visit https://gitforwindows.org.

Go to this link

Prior to having a bool type, Python didn't even have True/False keywords.

The reason something silly like `4 + True` works is because the bool type implements `tp_as_number` [0]. The reason it works this way is intentional because it would been a Python 3 str-style debacle if ints and bools were not interchangeable.

[0] https://github.com/python/cpython/blob/main/Objects/boolobje...

There's no mention of what Python version this is actually in.

After some digging, it looks like the answer is 3.14 [0], so we won't be seeing this until October [1].

One could argue that this is a security fix (just read the first sentence of the blog post) and should be included in all the currently supported Python versions (3.9+) [2].

[0] https://github.com/python/cpython/blob/main/Doc/whatsnew/3.1...

[1] https://peps.python.org/pep-0745/

[2] https://devguide.python.org/versions/

Then one day, the upcoming CPython 3.13.3 release, which we've taken on the e6dfa9d commit, came to our attention. I usually check projects in the Debug configuration. However, this time, I was curious and wanted to run the analysis in the Release configuration to see what happens in the interpreter shipped to the end user.

❯ CFLAGS="-I$(brew --prefix openssl)/include -I$(brew --prefix bzip2)/include -I$(brew --prefix readline)/include -I$(xcrun --show-sdk-path)/usr/include" \ LDFLAGS="-L$(brew --prefix openssl)/lib -L$(brew --prefix readline)/lib -L$(brew --prefix zlib)/lib -L$(brew --prefix bzip2)/lib" \ pyenv install --patch 3.7.2 < <(curl -sSL 'https://github.com/python/cpython/commit/8ea6353.patch?full_index=1') python-build: use [email protected] from homebrew python-build: use readline from homebrew Installing Python-3.7.2... patching file 'Misc/NEWS.d/next/macOS/2020-06-24-13-51-57.bpo-41100.mcHdc5.rst' patching file configure patching file configure.ac python-build: use tcl-tk from homebrew python-build: use readline from homebrew python-build: use zlib from xcode sdk Installed Python-3.7.2 to /Users/ryanlee/.pyenv/versions/3.7.2

GitHub Reference material: https://github.com/Fortune-Ndlovu/Intelligent-Support-Ticket-Routing-with-NLP-and-XGBoost/tree/main This notebook assumes you have the latest Python and the following libraries installed. First things first, Set Up Your Environment (Anaconda) by creating a new conda environment you can achieve this by opening up your terminal (or Anaconda Prompt):

If python is not installed in your PC then you can download it from https://www.python.org/

from concurrent.futures import ThreadPoolExecutor, as_completed from tqdm import tqdm urls = [ 'https://example.com', 'https://www.python.org', 'https://www.wikipedia.org', # Add more URLs... ] results = [] with ThreadPoolExecutor(max_workers=10) as executor: futures = {executor.submit(scrape_page, url): url for url in urls} for future in tqdm(as_completed(futures), total=len(futures), desc="Scraping"): results.append(future.result())

Comma Separated Value (CSV) files are common types of data files that are used in data analysis to help organize and structure data for storage. If you’re someone who works with financial records, customer information or even machine learning datasets, CSV files are a go to format because of their simplicity and the fact that they can be imported into almost any tool including Microsoft Excel, Google Sheets and even Python.

Yep, I'm in this camp. In particular, since the bin/lib dichotomy doesn't work well for everything[1], I'll put things free-form in /opt as needed, and then symlink executable entry points at /usr/local/bin. [2]

[1] e.g. if you want to install Python applications in isolated virtual environments, the venv has its own* bin/ and lib/ internally - you wouldn't want to flatten that structure and have all those applications share top-level /usr/local/bin and /usr/local/lib, because that would involve manually destroying the venv structure and also defeating the entire purpose of them.

[2] although this is not completely smooth if, like me, you want to compile multiple versions of Python from source and then make venvs based off of them - see https://github.com/python/cpython/issues/106045 .

datetime handling can absolutely be a hot spot, especially if you're parsing or formatting them. Even for relatively simple things like "parse a huge csv file with dates into dataclasses".

In particular, default implementation of datetime in cpython is a C module (with a fallback to pure python one) https://github.com/python/cpython/blob/main/Modules/_datetim...

Not saying it's necessarily justified in case of this library, but if they want to compete with stdlib datetime in terms of performance, some parts will need to be compiled.