Due to its popularity and range of uses, Python is one of the fastest-growing languages for data scientists and developers alike. Within data science, Python is known for being a beginner-friendly language that offers users a multitude of free and easily accessible resources. However, knowledge of Python doesn’t end with learning the programming language itself. There are several tools and environments, as well as multiple methods and styles of Python programming, that beginner data scientists can use to increase their skills and improve their chances of building a data science career.
The Python Integrated Development and Learning Environment, or IDLE, references the process of using this environment to write code with a Python shell and editor. Writing code with IDLE works well for beginner data scientists because, unlike more traditional terminals, using IDLE in the Python shell simplifies the process of identifying coding errors, editing projects, and managing workflows. IDLE is an essential skill for beginner data scientists looking to write faster and more efficient code.
What is IDLE Programming?
Python Integrated Development and Learning Environment (IDLE) is a multi-component space used to edit files and create programs using Python. As one of many open-source data science tools, IDLE is freely available to any Python user with access to the programming language. IDLE is a development and learning environment with two main window types: a Shell window and an Editor Window. The Shell window is used to interpret Python programs and code, as well as to read commands and statements. The Editor window is used to write and make corrections to the code.
There are many environments to choose from when learning how to program with Python, and it is much more common to see a data science curriculum focused on notebooks and terminals. In contrast to widely used environments like Jupyter Notebook, which many beginners and more advanced data scientists use to write and edit their code, IDLE comes pre-packaged with Anaconda, making it easily accessible to new users. Unlike Jupyter, IDLE does not require a browser or internet access to operate, which also means that it can be used in a wide range of activities and exercises.
Why Do Data Scientists Use IDLE?
Data scientists tend to use IDLE when they need a simple environment to write and edit their code. In addition to the shell and file editor, some of IDLE’s most important features are the debugger tool, workflow management, and customization and control tools.
File Editor
Data scientists primarily use IDLE to edit their files, as IDLE has a full menu of options for writing and editing code. Similar to traditional office tools, IDLE allows you to cut, copy, and paste code, as well as to “undo” or “redo” a program, statement, or command (among other functions). The file editor can also be used to practice running lines of code, as well as to import Python files from other projects and environments, making it easier for beginner data scientists to navigate and make changes to their code.
Workflow Management
Within data science, workflow management is the process of creating models, or a more efficient, structured method of organizing the data science lifecycle. Many Python environments come pre-programmed with tools that make it easier to engage in workflow management, which is another reason many data scientists prefer Python. The IDLE environment includes features that speed up the process of writing code, such as automatic indentation and code completion. Automatic indentation creates new lines of code whenever you need to start a new block, whereas code completion anticipates the code that you will write and finishes the line before it is completed. Each of these auto-complete features allows beginner data scientists to save time and learn more about the process of writing code.
DEBUG Code and Errors
Debugging, or the process of checking code for errors, can take up an extensive amount of time for data scientists, even more so for beginners with less knowledge of common coding mistakes. One of IDLE’s features allows you to turn on a Debugger in the shell which will search for any issues with your code. The debugger appears as a pop-up window in which you can analyze each line of your code by executing one line of code, checking the outcome, and then moving on to the next. The debugger can also analyze the data in your files, with a focus on potential issues with values and variables, as well as pinpoint the exact place in your code where a mistake has been made.
Customization and Control
IDLE allows you to customize the appearance of the shell and control its features. The Customization window gives Python users access to a complete menu of options to change default settings for everything from the environment font to the color, keys, and extensions, making it possible to color-code specific functions and operations in ways that have meaning for the project you are developing.
Is IDLE the Python Environment For You?
Whether you are new to Python or looking to advance your skills, programming with IDLE makes it easier to interpret and edit code. Noble Desktop’s Python classes and bootcamps offer hands-on training in working with the IDLE environment to improve your coding. The Python for Data Science Bootcamp introduces beginner data scientists to both IDLE and object-oriented programming. Noble Desktop also offers several data science classes that focus on using the latest Python tools; ideal for students interested in learning about other programming environments, such as Jupyter Notebook.