3  Python IDLE

Python IDLE is a simple IDE (Integrated Development Environment) that comes installed with the Python programming language. It’s designed to help write and execute Python code efficiently.

IDLE is named after Eric Idle, a member of the Monty Python comedy group, from which Python also gets its name.

3.1 Launching Python IDLE

After installing Python, launch IDLE from your operating system’s search menu:

  • Windows: Open the Start menu and type IDLE.
  • MacOS: Use Spotlight search (Cmd + Space), type IDLE, and press enter.
  • Linux: Find IDLE in your Applications menu or run idle from the terminal.

3.2 Simple Calculations

The IDLE shell window acts as an interactive interpreter, executing each line of code immediately when you press Enter.

Directly in the IDLE shell, you can input arithmetic expressions like 2 + 3 and press Enter to see the results immediately.

This feature makes IDLE ideal for quick calculations and experiments with Python’s mathematical capabilities, without needing to save or execute a script.

3.3 Python Scripts

Next, we create and run a simple Python script:

  1. Open a New File: Navigate in IDLE to File > New File to open an editor window.

  2. Enter Your Code: Type the following in the new editor window:

    print("Hello, Python!")

  3. Save Your Script: Save the file via File > Save As... naming it hello.py. Ideally, save it in your directory code within your toolkit-labs folder (refer to this explanation regarding our folder structure).

  4. Execute Your Script: Press F5 or go to Run > Run Module to execute your script. The output Hello, Python! should appear in the IDLE shell.

Now modify your script with varied messages. You can also Type help(print) or help() in the IDLE shell to explore Python’s extensive help documentation and learn about other functions.

3.4 What’s next?

Python IDLE provides an introduction to programming in Python, offering a straightforward interface for code development and execution. In one of the following sections, we are exploring more advanced IDEs like Visual Studio Code that support larger projects with additional features.

The next chapter will cover the installation and basic usage of Anaconda, a popular Python distribution that includes a robust package manager and a suite of pre-installed libraries commonly used in data science and machine learning.