Kotori
A flexible data historian based on InfluxDB, Grafana, MQTT, and more. Free, open, simple. (by daq-tools)
NumPy
The fundamental package for scientific computing with Python. (by numpy)
| Kotori | NumPy | |
|---|---|---|
| - | 301 | |
| 117 | 29,673 | |
| 0.9% | 1.1% | |
| 2.0 | 10.0 | |
| 2 months ago | 1 day ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
Kotori
Posts with mentions or reviews of Kotori.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning Kotori yet.
Tracking mentions began in Dec 2020.
NumPy
Posts with mentions or reviews of NumPy.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-04-24.
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How to Get Started with Scikit-Learn: A Beginner-Friendly Guide to Machine Learning in Python
As is the case with most Python libraries, it is open-source and free-to-use, making it easily accessible by anyone willing to learn machine learning, and it is built upon other open-source libraries within Python, like SciPy for advanced scientific operations, NumPy for efficient numerical computations, Matplotlib for data visualization, and Cython for increased efficiency and speed, similar to that of C/C++.
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It is not a compiler error. It is never a compiler error (2017)
I hit a similar issue in 2017 which is still the case today: Python's builtin `random.shuffle` destroys numpy arrays passed into it [0]. This is apparently a design limitation within numpy and cannot be detected or fixed, so it still stands today.
[0] https://github.com/numpy/numpy/issues/10215
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Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis.
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Back To Basics: git
In my testing, I found that when checking out 500 commits sequentially from the numpy repository, disabling this feature required 13.8 seconds to complete on average across 10 runs. Enabling this feature took on average 11.2 seconds across 10 runs. Not an astounding difference in testing, but if core.fsmonitor can save me 2.6 seconds per 500 commits, on a project with 37,775 commits that could add up to a time savings of 211.54 seconds, or 3 minutes and 32 seconds! More testing on my end needs to be done if this feature scales linearly, but for now I will keep it on and use version 1 of the tool.
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LAPACK in your web browser
Readers of this blog who are familiar with LAPACK are likely to not be intimately familiar with the wild world of web technologies. For those coming from the world of numerical and scientific computation and have familiarity with the scientific Python ecosystem, the easiest way to think of stdlib is as an open source scientific computing library in the mold of NumPy and SciPy. It provides multi-dimensional array data structures and associated routines for mathematics, statistics, and linear algebra, but uses JavaScript, rather than Python, as its primary scripting language. As such, stdlib is laser-focused on the web ecosystem and its application development paradigms. This focus necessitates some interesting design and project architecture decisions, which make stdlib rather unique when compared to more traditional libraries designed for numerical computation.
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1MinDocker #6 - Building further
numpy
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F1 FollowLine + HSV filter + PID Controller
This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays.
- Dia 12 - 1.2 Oito grandes ideias sobre arquitetura de computadores
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The Fastest Mutexes
https://github.com/numpy/numpy/issues/26510#issuecomment-229...
And now that I look at that again I realize I forgot to finish that up!
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Intro to Ray on GKE
The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as we’ve explored here, but Ray Clusters can also be created independent of Kubernetes.
What are some alternatives?
When comparing Kotori and NumPy you can also consider the following projects:
MerkavaDB - A fast ordered NoSQL database.
mitmproxy - An interactive TLS-capable intercepting HTTP proxy for penetration testers and software developers.
Dask - Parallel computing with task scheduling
SymPy - A computer algebra system written in pure Python
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