Python Decisions and Loops

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infoWhy this? More sophisticated control flow allows programs to make complex decisions and repeat actions reliably. Boolean expressions, nested selection, disciplined loop design, tracing, and systematic test data strengthen programming precision and debugging.

scheduleWhy now? Having created simple Python programs, we are ready to revisit selection and iteration in greater depth and apply them to interactive and control-system scenarios. This fluency is needed before we organise logic into functions and use loops to process lists.

The Python comparison operators are "==" (equal to), "!=" (not equal to), "<" (less than), "<=" (less than or equal to), ">" (greater than), and ">=" (greater than or equal to), and they evaluate expressions to True or False.
In Python, "==" compares two values for equality, whereas "=" assigns a value to a variable.
Boolean expressions can combine conditions to control program flow.
Python uses the lowercase Boolean operators "and", "or", and "not".
The Boolean operator "and" is True only when both conditions are True.
The Boolean operator "or" is True when at least one condition is True.
The Boolean operator "not" reverses a Boolean value.
Two-input truth tables can be used to verify compound Boolean conditions.
Indentation determines which statements belong to each branch or loop.
The Python keywords "if", "elif", and "else" handle mutually exclusive branches.
Nested selection places one decision structure inside another.
Python "for" loops are suited to definite repetition.
Trace tables reveal how variables change through each iteration.
Python "while" loops require condition updates to avoid infinite loops.
Test cases should include normal, boundary, and invalid inputs.
A simple control system can be modelled with a state variable, sensor-style input, decision rules, and an output action.

rocket_launchYou must be able to

Write multi-condition decisions that produce the intended output for each combination of conditions.
Build "for" and "while" loops with correct repetition conditions and updates.
Use trace tables and normal, boundary, and invalid test cases to validate and debug programs.
Refine a small interactive program using branching and repetition.
Model a simple control-system scenario in code using sensor conditions, state updates, and output decisions.

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