Previous lesson: Basic programming terminology
This lesson introduces a variety of ways that you can interact with programming languages like R and Python. It describes command-line interfaces (CLIs) and graphical user interfaces (GUIs) – in particular integrated development environments (IDEs) that make it easier to write and debug code. The Jupyter notebook system for literate programming is demonstrated and the lesson concludes with a discussion of package managers.
Learning objectives At the end of this lesson, the learner will:
Total video time: 50m 53s
Term covered is: environment.
Term covered is: integrated development environment (IDE)
If you are interested in installing the simple Thonny IDE demonstrated here, see this page.
It will generally not be necessary to have the Thonny IDE to do the lessons in this series. However, if you want to experiment with using the Python shell, installing Thonny and using the shell pane may be the easiest way to accomplish that. If you are going to use the Colab cloud platform for the lessons, there may be occasional cases where you want to execute a script on you local computer rather than in the cloud. Thonny is probably the easiest way to accomplish that since installing it is much faster and easier than installing the full Anaconda distribution.
The Spyder IDE is included in the Anaconda distribution. See the next lesson for more details on Anaconda.
RStudio can be installed separately or as part of the Anaconda distribution. See the next lesson for more details.
Terms covered are: literate programming and Jupyter notebook.
There are many ways to run Jupyter notebooks. Comparing these methods is the major focus of the next lesson.
For an introduction to using Markdown to format text, see this lesson
Terms covered are: function, argument, return value, and open source.
Terms covered are: importing, standard library, and module.
Terms covered are: package manager, repository, PyPI, CRAN, PIP, and Conda.
If you aren’t sure how you are going to be running your code, see Installing a programming environment
If you want to start coding Python as quickly as possible, see Quickstart guide for running Python in a Colab notebook
If you want to start coding R as quickly as possible, see Quickstart guide for running R in RStudio Cloud
If you want to code using the AI tool GitHub Copilot, see Setting up VS Code to use Jupyter notebooks and GitHub Copilot
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