2 - Control Flow and Python Data Structures — Topics & Learning Outcomes

Module Topics

Conditional Statements

• The if Statement — The if statement is the most fundamental conditional construct in Python, executing a block of code only when a specified condition is True.
• The elif Clause — The elif (else-if) clause allows a program to check multiple conditions in sequence after an initial if condition fails.
• The else Clause — The else clause defines a default block of code that executes when none of the preceding if or elif conditions are True.
• Comparison and Logical Operators — Conditions in Python are built using comparison operators (==, !=, <, >, <=, >=) and logical operators (and, or, not) to form complex Boolean expressions.
• Nested Conditionals — Conditional statements can be placed inside other conditional blocks to test additional conditions when an outer condition is True.

for Loops and the range() Function

• The for Loop — A for loop in Python iterates over each item in a sequence — such as a list, string, or range — executing the loop body once per item.
• The range() Function — The range() function generates an immutable sequence of integers and is commonly used with for loops to repeat an action a specific number of times.
• Iterating with an Index Using enumerate() — The enumerate() function allows a for loop to access both the index and the value of each item in an iterable simultaneously.
• Looping Over Numeric Ranges in Engineering — Engineering applications frequently require iterating over a defined numeric range, such as time steps in a simulation or indices in a data array.

while Loops

• The while Loop Structure — A while loop continuously executes its body until its controlling condition evaluates to False, checking the condition before each iteration.
• Infinite Loops and Loop Guards — An infinite loop occurs when a while loop's condition never becomes False, causing the program to run indefinitely without halting.
• Comparing for and while Loops — Choosing between a for loop and a while loop depends on whether the number of iterations is known in advance or determined dynamically during execution.

Loop Control: break and continue

• The break Statement — The break statement immediately terminates the innermost loop it appears in, transferring control to the first statement after the loop.
• The continue Statement — The continue statement skips the remainder of the current loop iteration and jumps directly to the next iteration's condition check.
• The else Clause on Loops — Python allows an optional else clause on both for and while loops, which executes only if the loop completed normally without encountering a break statement.
• Practical Use Cases for break and continue — In engineering and data processing, break and continue are frequently used to handle exceptions in data, implement early-exit optimizations, and process conditional sequences.

Lists

• Creating and Accessing Lists — A list is created using square brackets and can store any mix of data types. Individual elements are accessed using zero-based integer indices.
• Modifying Lists — Because lists are mutable, their elements can be changed, added, or removed after the list is created using built-in methods and direct assignment.
• List Slicing — Slicing extracts a sub-list from a list using the syntax list[start:stop:step], returning a new list containing the selected elements.
• List Comprehensions — A list comprehension provides a concise, readable way to create a new list by applying an expression to each item in an iterable, optionally filtered by a condition.
• Common List Operations and Methods — Python provides a rich set of built-in functions and list methods for sorting, searching, counting, and transforming list data.

Tuples

• Creating and Accessing Tuples — Tuples are defined using parentheses (or simply commas) and support the same indexing and slicing operations as lists, but their contents cannot be modified.
• Immutability of Tuples — Once a tuple is created, its elements cannot be added, removed, or changed, making tuples a safer choice when data integrity is important.
• Tuple Packing and Unpacking — Tuple packing assigns multiple values into a single tuple, while tuple unpacking assigns the elements of a tuple to individual variables in one statement.
• When to Use Tuples vs. Lists — Tuples are preferred when the data represents a fixed record or should be protected from modification, while lists are better suited for collections that need to grow or change.

Dictionaries

• Creating and Accessing Dictionaries — Dictionaries are created using curly braces with colon-separated key-value pairs. Values are retrieved by supplying the corresponding key in square brackets.
• Modifying Dictionaries — Dictionaries are mutable; entries can be added, updated, or deleted at any time using direct assignment and built-in methods.
• Iterating Over Dictionaries — Python provides dedicated methods for iterating over a dictionary's keys, values, or key-value pairs using keys(), values(), and items().
• Engineering Applications of Dictionaries — Dictionaries are well suited for mapping labeled engineering data, such as material properties, configuration parameters, or named measurement channels.

Sets and Choosing the Right Data Structure

• Creating and Using Sets — A set is an unordered collection of unique elements defined with curly braces or the set() constructor. It automatically discards duplicate values.
• Set Operations — Sets support mathematical operations including union, intersection, difference, and symmetric difference, enabling powerful comparisons between groups of data.
• Membership Testing with Sets — Sets provide O(1) average-time membership testing using the in operator, making them significantly faster than lists for checking whether a value exists in a large collection.
• Comparing Core Data Structures — Lists, tuples, dictionaries, and sets each have distinct properties — ordered vs. unordered, mutable vs. immutable, indexed vs. keyed — that make them appropriate for different tasks.
• Selecting the Right Structure for Engineering Problems — Choosing the appropriate data structure improves program correctness, performance, and readability, particularly in data-intensive engineering applications.

Student Learning Outcomes

By the end of this module, students will be able to:

Course Outcomes (reference)