Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
Figure Eight
CoffeeScript
Octoparse
Diggernaut
eScraper
Agenty
Typescript
JavaScript
artoo.js
Pandas
CoffeeScriptPandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.
CoffeeScript may be recommended for developers maintaining legacy CoffeeScript projects, or for those who prefer its syntax over JavaScript and are working on small projects. It might also be useful for educational purposes to understand how language features influence each other.
Based on our record, Pandas should be more popular than CoffeeScript. It has been mentiond 231 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.
Feature transformations should be deterministic: The same input should produce the same output when the same feature definition and configuration are applied. This is what allows training, backtesting, and live inference to remain aligned. Tools such as Pandas, Spark, or feature platforms such as Feast can be used to implement that logic. - Source: dev.to / about 1 month ago
For early-career security practitioners (0-3 years). Start with Python literacy if you do not have it. The free Python Crash Course book and the pandas getting-started guide are enough to bootstrap. Then a hands-on applied course: GTK Cyber's Applied Data Science & AI for Cybersecurity and SANS SEC595 are both reasonable starting points. The goal at this stage is to be able to load a Zeek conn.log into a pandas... - Source: dev.to / about 2 months ago
Python and data engineering for security data. Pandas for ingesting Zeek, Sysmon, EDR, and SIEM exports. Timestamp normalization to UTC, join keys across heterogeneous sources, feature extraction from raw logs. Without this layer, the ML content downstream is theater. - Source: dev.to / about 2 months ago
Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / about 2 months ago
Pandas url is the most widely used library for data manipulation. - Source: dev.to / 2 months ago
Not literally. And I would hardly say it was a matter of language superiority. I love Ruby myself. But Github was a lot simpler when it was still just a Rails app. But Rails was SSR by default, and most of the frontend was just Embedded Ruby (ERB) template files all over the place. And way back when, it was even relatively common to use Javascript supersets like CoffeeScript[1] and Opal[2]. The latter being Ruby... - Source: Hacker News / about 1 month ago
Surely coffeescript would have been more appropriate? [0]: https://coffeescript.org/. - Source: Hacker News / 6 months ago
My personal take is this would be like JavaScript adopting an optional Coffeescript[1] syntax. It's so different that it seems odd to make it an option vs a new language, etc. [1] https://coffeescript.org/#introduction. - Source: Hacker News / 7 months ago
JS isn't perfect, but it's good enough. And there is ongoing effort to make it even better. Also, many other languages compile to JS (without WASM). Notably: - https://www.typescriptlang.org/ - https://coffeescript.org/ - https://clojurescript.org/ - https://www.transcrypt.org/ I wrote https://multi-launch.leftium.com, which is only 6% JS. The majority is Svelte (65%) + TypeScript (27%). ( - Source: Hacker News / over 2 years ago
As a front-end web developer, do you still use CoffeeScript or jQuery? Unlikely, as TypeScript, ES/TC39 and Babel (and the retirement of Internet Explorer thanks to @codepo8 and his EDGE team) have helped to transform JavaScript into some kind of a modern programming language. - Source: dev.to / over 3 years ago
NumPy - NumPy is the fundamental package for scientific computing with Python
Octoparse - Octoparse provides easy web scraping for anyone. Our advanced web crawler, allows users to turn web pages into structured spreadsheets within clicks.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Diggernaut - Web scraping is just became easy. Extract any website content and turn it into datasets. No programming skills required.
OpenCV - OpenCV is the world's biggest computer vision library
eScraper - eScraper is an eCommerce data scraping tool that collects data from multiple sites and prepares a relevant .csv or excel file with all product info for your stores, whether its, PrestaShop, Magento, WooCommerce, or Shopify store.