Snap-in-Time
NumPy
Snap-in-Time | NumPy | |
---|---|---|
4 | 272 | |
6 | 26,413 | |
- | 1.1% | |
3.8 | 10.0 | |
about 1 year ago | 5 days ago | |
Python | Python | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Snap-in-Time
-
Completing Advent of Code 2015 with 3 Programming languages
Python - it’s my main language and the one in which I’m most proficient. I have written some programs used by many others (like my Extra Life Donation Tracker) and utilities that solve some problem I have (like my btrfs snapshot and backup program). By solving each problem in Python first, I allow myself to focus on the problem first instead of a syntax I’m unfamiliar with.
-
how do you go from basic python stuff to building a project????
https://github.com/djotaku/Snap-in-Time - for btrfs snapshots
-
Do programmers save chunks of code for repeated use?
IF you think it'll be useful to others, then see if there's a repo for your programming language like CPAN, PyPi, NPM, etc and put it there. This utility I made for btrfs snapshots is useful to anyone else using btrfs, so I put it on pypi: https://github.com/djotaku/Snap-in-Time can be found at https://pypi.org/project/snapintime/
-
Dumb Question: learning to code but have no idea what to code
I wanted to manage my btrfs COW snapshots, so I madde: https://github.com/djotaku/Snap-in-Time
NumPy
-
Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
-
Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
-
JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
-
Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
-
A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
-
Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
-
NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
adventofcode - My solutions to the Advent of Code challenges
SymPy - A computer algebra system written in pure Python
pip - The Python package installer
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
aoc2015 - Advent of Code 2015
blaze - NumPy and Pandas interface to Big Data
Glitch-Garden - A Plants Vs Zombies clone from my Udemy Class
SciPy - SciPy library main repository
yabsnap - Btrfs Scheduled Snapshot Manager for Arch
Numba - NumPy aware dynamic Python compiler using LLVM
ELDonationTracker - A Python-based donation tracker for Extra Life streams
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).