Chapter 5: Execution Control

Controlling the flow of your program is essential for writing effective data science code. Python provides powerful control structures for loops, conditionals, and exception handling that let you manage how your code executes.

For Loops: Iterating Over Data

The for loop is the workhorse of data iteration in Python. It allows you to process each item in a collection efficiently.

Using range() to Generate Numbers

The built-in range() function creates an iterable sequence of numbers:

Simple For Loop with Range

Use range() with for to repeat an action multiple times:

Notice that range(1, 6) starts at 1 and stops before 6, giving you five iterations.

Iterating Over Lists

The most common pattern in data science - iterating over a list:

Iterating with Index and Value

Use enumerate() to get both the index and value:

Iterating Over Multiple Lists

Use zip() to iterate over multiple lists simultaneously:

List Comprehensions

A concise way to create lists using for loops:

List comprehensions are faster and more Pythonic for simple transformations.

While Loops: Repeating Until a Condition

While loops continue executing until a condition becomes false. They're useful when you don't know how many iterations you need in advance.

Basic While Loop

While Loop with Generator

Generators can be used with while loops for efficient iteration:

The else clause runs when the while condition becomes false (but not if you break out).

Infinite Loop with Break

Sometimes you want an infinite loop that exits based on a condition:

Exception Handling: Expecting the Unexpected

Robust code handles errors gracefully. Python's try/except mechanism lets you catch and handle exceptions.

Basic Try/Except

Catch specific exceptions to handle errors:

This prevents your program from crashing when the list is empty.

Catching Multiple Exception Types

Handle different exceptions differently:

Using Finally for Cleanup

The finally block always executes, even if an exception occurs:

Getting Exception Details

Access the exception object for more information:

Conditionals: Making Decisions

Conditional statements let your code make decisions based on data.

If/Elif/Else Blocks

The standard way to branch between multiple conditions:

Comparison Operators

Python supports rich comparison operators:

Ternary Conditional Expression

Single-line conditional assignment:

Multiple Conditions with and/or

Combine conditions logically:

Checking Membership

Use in to check if an item exists in a collection:

Break, Continue, and Pass

Special keywords that control loop execution.

Using Break to Exit Early

Exit a loop immediately:

Using Continue to Skip Iterations

Skip the rest of the current iteration and continue with the next:

Only "Moby Dick" gets printed because continue skips the print for other whales.

Using Continue for Filtering

A practical use case - processing only valid data:

Using Pass as a Placeholder

pass does nothing - it's a placeholder for future code:

Pass in Conditional Blocks

Sometimes you need an empty branch:

Combining Control Structures

Real-world code often combines these control structures.

Nested Loops with Conditionals

Exception Handling in Loops

While Loop with Multiple Exit Conditions

Key Takeaways

For Loops: Use for iterating over sequences (lists, ranges, etc.)
While Loops: Use when you don't know how many iterations you need
Try/Except: Always handle potential errors in production code
If/Elif/Else: Use for branching logic based on conditions
Break: Exit a loop immediately
Continue: Skip to the next iteration
Pass: Placeholder for empty code blocks

These control structures are the building blocks of algorithm implementation. Master them, and you can express any computational logic clearly and efficiently.

Quiz

📝 Test Your Knowledge: Chapter 5: Execution Control

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