![]() ![]() Programs with graphical interface are often referred by the acronym GUI, which means graphical user interface. Henceforth, you will be able to integrate your programs to the efficient command line, once they start receiving values as input from a command line interpreter (shell). The command line is, therefore, still a modern resource for automation. The possibility of storing sequences of commands as macros or in a script allows repeating solutions using multiple programs as if they were subroutines in a programming language. In particular, the command line allows noticing how the operation of graphical interfaces are limited (and inefficient). The efforts required to learn it will be rewarded over time. Instead of fearing or running from the command line, it is worth embracing it as a programming tool. In 2022, the command is still effective and efficient to perform common programming tasks.Īutomation, data exchange, data conversion, and server operations are some examples.Įven in personal projects, the command line is required to use package managers (as the ones presented in Libraries) and source-control management systems. Old School Automation for the Futureįor many people, the command line is an archaic magical artifact an inheritance of the dawning of Computation. In many languages, it suffices to follow the models from the experimentation section to modify syntax, commands and functions from the code blocks.Ĭ and C++ are exceptions, for they require pointers access the memory. If you want to use another language, the introduction provides links for configure development environments for the C, C++, Java, LISP, Prolog, and SQL (with SQLite) languages. The following example assumes that you know how to run code in your chosen language, as presented in the configuration pages. Thus, if you have an Integrated Development Environment (IDE), or a combination of text editor and an interpreter, you are ready to start. If you need to configure one, you can refer to the following resources. Thus, sooner or later, you will need to set up a development environment. However, they do not provide all features offered by interpreters for the languages. If you wish to try programming without configuring an environment, you can use of the online editors that I have created: Later, I have commented about GDScript as an option for people who want to program digital games or simulations.įor the introductory programming activities, you will need, at least, a development environment configured for one of the previous languages. Just give it some time.In the introduction to development environments, I have mentioned Python, Lua and JavaScript as good choices of programming languages for beginners. Feeling frustrated with the new technology is natural during the learning curve period. It takes 1 to 2 years to really get used to a new system. Wishing a new language and the surrounding technology was more like something you are already familiar with is a common experience when learning a new language. ![]() OP just needs some time to get used to a different workflow. I don't know of such a technology for Stata, but regardless, I don't happen to think notebooks provide all that much more quality of life. Jupyter is indeed a small improvement in quality of life for data science, especially if you've ever written python in an IDE like pycharm or a text editor like emacs or (if you are especially unlucky) IDLE. ![]() I think this might help to explain some of the confusion around what OP is asking about. ![]() Executing code in a notebook style environment is definitely useful in data science, but it is not the way python has typically been used over its lifespan, and jupyter notebooks would be downright cumbersome when doing software development. I think it might be helpful to point out that there is an important difference between the language (python) and the text editing software that is used to write code (in this case Jupyter). Based on #4, it sounds like Dev is talking about Jupyter notebooks a relatively new innovation in anaconda python that resembles a marginally smarter version of R markdown. ![]()
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