Top 23 Python Numpy Projects

datascienceipythonnotebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikitlearn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
data science ipython notebooks

Now we can see that the numpy library has been added to the packages. That way we can install the libraries and packages required for the project.

Scout APM
Scout APM: A developer's best friend. Try free for 14days. Scout APM uses tracing logic that ties bottlenecks to source code so you know the exact line of code causing performance issues and can get back to building a great product faster.

datasets
🤗 The largest hub of readytouse datasets for ML models with fast, easytouse and efficient data manipulation tools
Project mention: Hugging Face Introduces ‘Datasets’: A Lightweight Community Library For Natural Language Processing (NLP)  reddit.com/r/artificial  20211108Code for https://arxiv.org/abs/2109.02846 found: https://github.com/huggingface/datasets

Project mention: How to load 85.6 GB of XML data into a dataframe  reddit.com/r/pythontips  20211201
I’m quite sure dask helps and has a pandas like api though will use disk and not just RAM.

Project mention: Can anyone recommend resources to prepare for Pandas and Numpy interview questions?  reddit.com/r/datascience  20210924

if you have just a few methods that need to be sped up, you could also consider Numba

Project mention: [D] Paper Explained  Sparse is Enough in Scaling Transformers (aka Terraformer)  Video Walkthrough  reddit.com/r/MachineLearning  20211201
Code: https://github.com/google/trax/blob/master/trax/examples/Terraformer_from_scratch.ipynb


Fast Array Manipulation in Python: Since Numpy is the de facto standard for storing multidimensional data, any performance gain you see using librapid math kernels will need to be realized on data which probably started its life as a numpy array, and needs to be passed to another tool as a numpy array. Hopefully there will be (or already is?) a way to build a librapid array out of a numpy array without copying the data and vice versa. In fact I might suggest that librapid focus on the fast math operations and simply become an accelerator for numpy arrays. For instance, look at CuPy which provides GPUimplemented operations within a numpycompatible API, and Bottleneck which simply provides fast Cbased implementations of some otherwise slow parts of Numpy. Also note that numpy *can* be multithreaded depending on the operation and some environment variables. Singlethreaded to Singlethreaded I think you will be hardpressed to beat Numpy on general math operations, but that doesn't mean there aren't specific "kernels" that are more specialized that can be greatly improved with a C++ backend.

This should get you back to a more intuitive understanding: https://github.com/arogozhnikov/einops It don't reshape/flatten/reduce without it now. I'd advise that you take the time to read and practice along the tutorial.

"On the simpler side". Do you mean with a graphical interface? Then, orange would be a nice solution. https://orangedatamining.com/

Project mention: We built a pi controlled hydroponics box that grows your plants 1.5x faster using ML  reddit.com/r/raspberry_pi  20210426
but it looks like none of your plants are supported by the plantvillage model, or do I understand something wrong? https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/image_classification/plant_village.py#L57

Project mention: Rust is slow (in ways that matter to the most people)  reddit.com/r/rustjerk  20211023
A great example is orjson, which is faster and more correct than equivalent libraries written in C.


mars
Mars is a tensorbased unified framework for largescale data computation which scales numpy, pandas, scikitlearn and Python functions.

numpyro
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Perhaps an alternative to look into: Numpyro [1] has a JAX backend so can be really fast when compiled; and it can run on GPUs. So that might be helpful for your problem with loads of data.


There are also online & offline onset detection approaches available as part of the Python madmom library https://github.com/CPJKU/madmom as binaries and Python classes. The methods included in madmom have shown state of the art results in multiple Music Information Retrieval Evaluation eXchange (MIREX) campaigns in recent years. Hope that's useful to you.

Project mention: How to write a resume for python / ML jobs?  reddit.com/r/learnmachinelearning  20210206
my most useful project is yolo object detector implementation in tf2 and I'm currently working on 2 other projects, one of which is the implementation of various drl algorithms in tf and the other project will be based on the latter and it's concerned with trading. The rest are more of scripts rather than projects ex: web scraping, file management, programming challenges ...

GeneticAlgorithmPython
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Project mention: PyGAD 2.16.1 Released: An opensource Python library for building the genetic algorithm and optimizing machine learning models.  reddit.com/r/learnmachinelearning  20210929The user can use the tqdm library to show a progress bar. https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/50

Project mention: Change docstring style for a project's entire codebase  reddit.com/r/pycharm  20210517
Ah  I was thrown off by 'type' vs 'style'. You might want to take a look at https://github.com/dadadel/pyment

Re: Matchering (https://github.com/sergree/matchering), here is a little more information. If I recall correctly there is a dockerized version so you can run it locally relatively easily if you are willing to learn a couple of bash commands. I have not played with it a lot and it is separate from the DAW.

Project mention: [P] Fastest wavelet transforms in Python + synchrosqueezing  reddit.com/r/MachineLearning  20210505
Also see Kymatio for SOTA on timeseries with limited data, fast and differentiable; nice lecture.

PySR
Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing
Project mention: [D] Inferring general physical laws from observations in 300 lines of code  reddit.com/r/MachineLearning  20210802This is really neat! Since you're interested in this subject, you may also appreciate PySR and the corresponding paper which uses Graph Neural Networks to perform symbolic regression.
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Index
What are some of the best opensource Numpy projects in Python? This list will help you:
Project  Stars  

1  datascienceipythonnotebooks  21,875 
2  NumPy  18,946 
3  datasets  11,444 
4  Dask  9,239 
5  numpy100  7,541 
6  Numba  7,039 
7  trax  6,611 
8  cupy  5,568 
9  einops  3,914 
10  orange  3,108 
11  datasets  3,072 
12  orjson  2,538 
13  xarray  2,324 
14  mars  2,288 
15  numpyro  1,186 
16  Eliot  929 
17  madmom  828 
18  yolotf2  721 
19  GeneticAlgorithmPython  711 
20  pyment  670 
21  matchering  509 
22  kymatio  472 
23  PySR  443 
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