SciPy
NumPy
Pandas
Matplotlib
MATLAB
Scikit-learn
Seaborn
python docx
Julia
Python
MATLAB
GNU Octave
Scilab
Wolfram Mathematica
Rust
Sage Math
We recommend LibHunt Julia for discovery and comparisons of trending Julia projects.
Julia{"data_science" => "Data scientists who require a fast and flexible language for data manipulation and analysis.", "machine_learning" => "Developers looking to implement machine learning models that benefit from Julia's performance.", "numerical_analysis" => "Engineers and analysts conducting numerical analysis that demands high computational efficiency.", "scientific_computing" => "Researchers and scientists working on mathematical, statistical, and computational problems."}
Based on our record, Julia should be more popular than SciPy. It has been mentiond 127 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
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... - Source: dev.to / about 1 year ago
Python has become a popular programming language for different applications, including data science, artificial intelligence, and web development. But, did you know creating and rendering fully customized videos with Python is also possible? At Stack Builders, we have successfully used Python libraries such as MoviePy, SciPy, and ImageMagick to generate videos with animations, text, and images. In this article, we... - Source: dev.to / over 1 year ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / almost 2 years ago
SciPy: a library used for scientific and technical computing. It has a function that can calculate the cosine distance, which equals 1 minus the cosine similarity. - Source: dev.to / over 2 years ago
Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses... Source: over 2 years ago
Mine is Julia, although I don't use diary. Nowadays I like SuperCollider. https://julialang.org. - Source: Hacker News / 3 months ago
> I was active in the Python community in the 200x timeframe, and I daresay the common consensus is that language didn't matter and a sufficiently smart compiler/JIT/whatever would eventually make dynamic scripting languages as fast as C, so there was no reason to learn static languages rather than just waiting for this to happen. To be very pedantic, the problem is not that these are dynamic languages _per se_,... - Source: Hacker News / 3 months ago
Julia: Exceptional Numerical Processing. - Source: dev.to / 5 months ago
To use Julia – one of the best programming languages, which is unfairly considered niche. Its applications go far beyond HPC. It’s perfectly suited for solving a wide range of problems. - Source: dev.to / 5 months ago
In this post, I’m exploring dev tools for data scientists, specifically Julia and Pluto.jl. I interviewed Mandar, a data scientist and software engineer, about his experience adopting Pluto, a reactive notebook environment similar to Jupyter notebooks. What’s different about Pluto is that it’s designed specifically for Julia, a programming language built for scientific computing and machine learning. - Source: dev.to / 7 months ago
NumPy - NumPy is the fundamental package for scientific computing with Python
Python - Python is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming
Matplotlib - matplotlib is a python 2D plotting library which produces publication quality figures in a variety...
GNU Octave - GNU Octave is a programming language for scientific computing.