12/11/2022 0 Comments Anaconda vs pycharm![]() No matter you’re data scientists or software engineers, you always want to use a version control tool. When you change variable names, change function signatures, or delete files, it will allow you to do it systematically, which will prevent bugging due to these refactoring actions. If it's a reference to a variable, it'll direct you to the definition. If it's a definition itself, it'll prompt the usages. It's very convenient to look up any variable or functions with a shortcut (hold down Cmd or Ctrl and click). An important feature of code analysis is to inform you about the duplicates, which will help you refactor your code. It can check whether variables have been used or not, whether any imported modules are used or not, whether certain variables are used before their definitions, and various other analyses. There are also built-in short snippets that can automatically prompted, such as _init_ method for a class. The auto-completion suggestions are prompted quickly after you start to type. You'll learn the best practices for Python coding along the way. It can check if there are problems with the coding style, such as naming and indentation. Certainly, other IDEs have these features too, but there can be variation how good they are. It has the following features that I like to use. You don’t really need to download anything particular for JupyterLab, because once you have Anaconda up running, you can access it very conveniently within Anaconda, which will handle all the installations and other setups for you. Once it’s downloaded, just follow the prompts. Similarly, you need to pick the version for your own OS. Here’s the link to the comparison of different plans. ![]() But there are other versions for teams and enterprises. ![]() For many of us, we can just use the individual version. To install Anaconda, you can go to the Anaconda website. However, the Community version should work just fine if you mostly do Python development. You can take advantage of this benefit, if you’re in a similar situation. I work for a non-profit educational institute, so I have access to the Professional version. Depending on your OS, you need to download the correct version. To install P圜harm, you can go to the P圜harm website. I’ll try my best to be concise, because it’ll be overwhelming for beginners if I pour too much information. I’ll first introduce the installation and then discuss the role of each tool. Specifically, we’ll use three tools: P圜harm, Anaconda, and JupyterLab. But probably it’s something that you can try first if you have no ideas about your configurations. Certainly, it won’t be a one-size-fits-all solution for all of you. In this article, I’d like to share the combination that I’ve found to be suitable to my needs for my data science projects. In other words, your shopping list is too long and you’re probably lost where you should get started. The problem is that there are too many choices on the market, and for learning purposes, you may have already tried different tools. After you have set up your hardware, it’s time to think about how you should pick the software that you need to start your data science projects. It’s true to data science researchers too. Don’t get me wrong - we always want to improve our productivity - with the same amount of time, we can get more work done.
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