Pandas is a fabulous tool box for Data Science and Machine Learning, with a multitude of capabilities built-in . But what if there was a way to add your own custom functions to every dataframe? For a while now, pandas has offered a custom extension capability which allows classes to be created in your project that add new processing features into dataframes. But these classes are deeply tied to different projects and awkward to share with co-workers, let alone the wider data science community. This is where pandex comes in.
The Razy Trojan is targeting legitimate browser extensions and is spoofing search results in the quest to raid cryptocurrency wallets and steal virtual coins from victims. According to new research published by Kaspersky Lab, the malware, known as Razy, is a Trojan which uses some of the more unusual techniques on record when infecting systems. Razy is an executable file which spreads through malvertising on websites and is also packaged up and distributed on file hosting services while masquerading as legitimate software. The main thrust of the malware is its capability to steal cryptocurrency. Razy focuses on compromising browsers, including Google Chrome, Mozilla Firefox, and Yandex.
One of the most common question people ask is which IDE / environment / tool to use, while working on your data science projects. As you would expect, there is no dearth of options available – from language specific IDEs like R Studio, PyCharm to editors like Sublime Text or Atom – the choice can be intimidating for a beginner. If there is one tool which every data scientist should use or must be comfortable with, it is Jupyter Notebooks (previously known as iPython notebooks as well). Jupyter Notebooks are powerful, versatile, shareable and provide the ability to perform data visualization in the same environment. Jupyter Notebooks allow data scientists to create and share their documents, from codes to full blown reports.
Whenever someone says'You can do that with an extension' in the Jupyter ecosystem, it is often not clear what kind of extension they are talking about. The Jupyter ecosystem is very modular and extensible, so there are lots of ways to extend it. This blog post aims to provide a quick summary of the most common ways to extend Jupyter, and links to help you explore the extension ecosystem. JupyterLab is a popular'new' interface for working with Jupyter Notebooks. It is an interactive development environment for working with notebooks, code and data -- and hence extremely extensible.