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#numpy

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Sharlatan<p><a href="https://mastodon.social/tags/guix" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>guix</span></a> forging <a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> with a team</p><p><a href="https://codeberg.org/guix/guix/milestone/20775" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">codeberg.org/guix/guix/milesto</span><span class="invisible">ne/20775</span></a></p><p>One build system to build them all<br><a href="https://mastodon.social/tags/NumPy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NumPy</span></a> 2 and friends refresh as a bonus</p>
TCCI – Learn Coding & IT Skill<p>📦 New to Python? These 3 libraries are your best starting point:</p><p>1️⃣ NumPy — powerful numerical computing<br>2️⃣ Pandas — analyze &amp; manipulate data<br>3️⃣ Matplotlib — visualize data beautifully</p><p>These are essentials for data science &amp; programming beginners!</p><p>From TCCI – Tririd Computer Coaching Institute, Bopal Ahmedabad</p><p><a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://mastodon.social/tags/NumPy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NumPy</span></a> <a href="https://mastodon.social/tags/Pandas" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pandas</span></a> <a href="https://mastodon.social/tags/Matplotlib" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Matplotlib</span></a> <a href="https://mastodon.social/tags/Programming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Programming</span></a> <a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> <a href="https://mastodon.social/tags/FOSS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>FOSS</span></a> <a href="https://mastodon.social/tags/TCCI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TCCI</span></a> <a href="https://mastodon.social/tags/EdTech" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>EdTech</span></a> <a href="https://mastodon.social/tags/LearnPython" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LearnPython</span></a></p>
Blosc Development Team<p>🗣️ Announcing Python-Blosc2 3.6.1</p><p>!Unlock new levels of data manipulation with Blosc2! 🚀</p><p>We've introduced a major improvement: powerful fancy indexing and orthogonal indexing for Blosc2 arrays.</p><p>We've tamed the complexity of fancy indexing to make it intuitive, efficient, and consistent with NumPy's behavior. 💪 </p><p>Read all about it on our blog! 📝 <a href="https://www.blosc.org/posts/blosc2-fancy-indexing/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">blosc.org/posts/blosc2-fancy-i</span><span class="invisible">ndexing/</span></a></p><p>Compress Better, Compute Bigger!</p><p><a href="https://fosstodon.org/tags/Blosc2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Blosc2</span></a> <a href="https://fosstodon.org/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> <a href="https://fosstodon.org/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BigData</span></a> <a href="https://fosstodon.org/tags/NumPy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NumPy</span></a> <a href="https://fosstodon.org/tags/Performance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Performance</span></a> <a href="https://fosstodon.org/tags/HPC" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HPC</span></a></p>
Python Job Support<p>Numpy cheat sheet for data science <a href="https://mastodon.social/tags/numpy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>numpy</span></a> <a href="https://mastodon.social/tags/numpytutorial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>numpytutorial</span></a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a>&nbsp;<a href="https://mastodon.social/tags/datascientist" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascientist</span></a></p><p>Welcome to our comprehensive Numpy cheat sheet for data science video! In this guide, we cover essential Numpy concepts and ... source</p><p><a href="https://quadexcel.com/wp/numpy-cheat-sheet-for-data-science-numpy-numpytutorial-datascience-datascientist/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">quadexcel.com/wp/numpy-cheat-s</span><span class="invisible">heet-for-data-science-numpy-numpytutorial-datascience-datascientist/</span></a></p>
Simone Conradi<p>I move 1 along the x-axis, then rotate by an angle theta, I move 1/phi, rotate by theta, I move 1/phi², rotate by theta etc etc.<br>Made with <a href="https://mathstodon.xyz/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://mathstodon.xyz/tags/numpy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>numpy</span></a> <a href="https://mathstodon.xyz/tags/matplotlib" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>matplotlib</span></a></p>

🚀 Great news for OpenMP on Python!

NumPy 2.3 includes early OpenMP support, making sorting operations like np.sort and np.argsort faster by using multiple processor cores — a big step for performance!

🛠️ This new feature is off by default but can be turned on during installation with -Denable_openmp=true

This marks the beginning of more parallel computing support in NumPy!

phoronix.com/news/NumPy-2.3-Re

www.phoronix.comNumPy 2.3 Introduces OpenMP Parallelization SupportNumPy 2.3 is out today as the latest release of this widely-used library for scientific computing

If you're writing python libraries, DON'T REQUIRE fileno ON FILE OBJECTS!

Dealing w/ the bullshit that numpy.fromfile wants the fileno attribute on a file object. Yes, it's slightly faster, but it also makes it harder to mock when doing testing.

Now I'm going to have to deal w/ creating a temporary directory, writing the file, and cleaning up afterward. Things that unittest.TestCase should have an option to do, but doesn't. Luckily I've dealt w/ this BS before, so I'll just copy the code from another project.

Since I couldn't figure out how to use numpy.take, and LLMs couldn't figure out how to do what I needed to do, I read the numpy slicing chapter, and I came up with the following:
indexes = np.arange(W * H)
rgb[0, i].flat = c[0].flat[np.array(reps[i].flat) * (W * H) + indexes]

EDIT: it was broken, needed to add in the position index and simplification.

I have finally decided to use #Typescript, #Deno, and #Assemblyscript to do my #SLM project. Typescript has strong typing. Assemblyscript will be compiled into #Webassembly wasm which is much faster. I don't need #pytorch or #numpy for manipulating matrices. NNUE modeling of language allows me to use arrays for #computing #GradientDescents.

While Javascript is a messy language, the other related script languages are clean and strict. #WebGPU allows me to use the #GPU via a browser too. #AI

🐍 I don’t like NumPy

「 NumPy is all about applying operations to arrays. When the arrays have 2 or fewer dimensions, everything is fine. But if you’re doing something even mildly complicated, you inevitably find yourself with some operation you want to apply to some dimensions of array A, some other dimensions of array B, and some other dimensions of array C. And NumPy has no theory for how to express that 」

dynomight.net/numpy/

DYNOMIGHT · I don’t like NumPyit’s too hard

Is it possible to do gradient descent in python without using numpy. Numpy array is a very strange animal that can result in unexpected results if you try to do some manipulation that other libraries, such as Matplotlib, don't like. On the other hand, python is a bad language for people who want to do simple affine transformation with arrays. Python list is not array and does not allow you to do addition or multiplication. And #python list and #Numpy ndarray are incompatible animals. #AI

Do you maintain or contribute to a #Python package that includes a C extension? Would you like to run a fuzzer against it?

If so, let me know and I will run it, or help you to get it running.

The fuzzer is #fusil, which generates random code calling into your functions and methods. It's useful to check for crashes on invalid inputs or unexpected call patterns.

It has found about 50 crashes in #CPython, 20 in #PyPy, 6 in #Numpy etc.

#fuzzing #fuzzer #testing
See here:
github.com/devdanzin/fusil/iss

GitHubFuzz C extensions · Issue #37 · devdanzin/fusilBy devdanzin

Connaissez-vous l'histoire du mec qui ne sait pas programmer mais que décide dans un moment de passion qu'il va implémenter un truc avec des vraies mathématiques (genre il y a des divisions et tout) en #python ?
Le même gars qui après une demi journée de rage et larmes pose une question tellement spécifique au moteur de recherche qu'il tombe sur l'implémentation déjà existante fournie par #numpy.

C'est à emporter, c'est pour un ami, moi je suis végétarien.