Are you using R and Python at the same time, or in different projects, or in the same project? Take a look at prython, an IDE specifically designed to meet your needs.
Forget about the network debate between R and Python.
If it might be reasonable to use both R and Python, what would happen in the real world?
What if the data science or analysis team is made up of people with expertise in these two languages? What if a system is designed to use libraries and packages in the ecosystem, and these libraries and packages are developed from two basic languages? What if a use case requires a certain implementation part to be separated from the language of the other parts of the product?
What if you want to use an IDE to implement and manage these different codes?
Or, what if you use R and Python at the same time in different projects and only need a single workspace to write all the code?
Use Python, an IDE designed for R and Python coding, even in the same project.
prython is a novel IDE. By connecting the panel in the canvas, you can code in R or Python (you can even use both languages in the same project). This allows you to organize your code, perform one-click running experiments, and display your charts and data frames next to the panel where they are created. Tired of remembering which lines need to be commented out to test something? Or do you just want to organize the code better? prython is currently available for Windows and runs with the local R/Python kernel.
The advantages of this project are all related to the easy-to-integrate Python and R programming environment, including code-free functions for input and output stream management, data visualization, spin-out context consoles at different locations in the project flow, mixed data frame checking, etc.
The biggest drawback of prython at present is that it can only run in Windows.
If you are a Windows user, you can download prython from the official website.
Prython is based on panels. Panels can be connected to each other on the canvas. Each panel runs Python or R code. The panel has connections including input and output, which can be configured and reconfigured as needed. The panel can run independently, or it can be connected in series with the front or back panel. Why bother about the panel?
Data professionals need to experiment with their data, make a lot of graphs, and divide the code into different areas. They rarely want a single linear script that runs from start to finish. Doing so will almost inevitably lead to very cluttered scripts, unclear output, multiple confusing charts, and which comments users need to remember to test certain content. No other IDE can fully meet this requirement.
According to the description on the prython website, the following are typical reasons for using prython:
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Track and describe experiments and tests. There is no need to remember what needs to be commented out in the script to test X changes, it is easy to do this with prython. -
Simultaneous display of results and charts in the canvas. -
Perform complex tests on different models with a single click (for example: you want to test multiple scikit-learn models at once). -
Split the code into different areas in the canvas. That is, load the input information into a part of the canvas, put the model on one area for training, and perform drawing/analysis in another area. -
Mix Python and R code into the same project. -
Visualize the changes and evolution of the data frame in the script. In R or Python, prython calculates all changes made to the data frame across panels, and whenever the data frame changes, it displays it as a table next to each panel.
The debate about which language dominates all languages has passed, but this does not mean that any language can be a "winner." Python and R are two tools used to achieve data science or data analysis goals. Setting goals based on the tools used can have counterproductive effects. So, use one or both of these two languages, learn about prython, and see if its unique approach to data science-centric IDEs can improve your efficiency. In the final analysis, what your tools should do is keep yourself from being disturbed and make them effective.
Author: Matthew Mayo | Translator: Sambodhi | Planning: Liu Yan | Transfer: InfoQ
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