Python Programming Course Overview

Overview

This lecture series introduces programming concepts using Python, starting with foundational topics like functions, variables, conditionals, loops, and advancing through exceptions, file I/O, regular expressions, and object-oriented programming. The course emphasizes practical problem-solving, best practices, and exposure to Python’s extensive features and libraries.

Course Introduction & Structure

• Course requires no prior programming experience.
• Begins with functions and variables, followed by conditionals, loops, exceptions, libraries, testing, file I/O, regular expressions, and object-oriented programming (OOP).
• Weekly lectures introduce concepts, followed by problem sets for hands-on practice.
• Visual Studio Code (VS Code) or other text editors can be used for coding.

Programming Basics: Functions, Variables, and Syntax

• Code is written as plain text, typically saved as .py files for Python.
• The print function outputs data to the screen and accepts arguments (inputs).
• Arguments influence function behavior, e.g., print("Hello World").
• Bugs are inevitable mistakes in code, often called syntax errors or logical errors.
• Syntax is strict; even small typos can cause errors.

User Input, Variables, and Data Types

• The input function retrieves user input (always as string/text).
• Variables store values for reuse; assignment operator (=) assigns values.
• To use user input in other functions, assign it to a variable and reference that variable.
• Comments (#) add notes to code for clarity; pseudocode outlines logic before coding.
• Strings are sequences of text; integers (int) are whole numbers without decimals.
• Convert input to integers (int(input())) before mathematical operations.
• Strings can be cleaned with methods like strip(), capitalize(), and title().

String Manipulation & Methods

• String concatenation uses + or formatting (e.g., f-strings for dynamic insertion).
• Multiple print arguments are separated by commas and add spaces automatically.
• String methods like strip(), capitalize(), title(), and split() help clean and process input.

Error Handling and Testing

• Syntax errors are fixed by correcting code structure.
• Runtime errors (like ValueError) occur during execution and can be caught with try/except blocks.
• Defensive programming anticipates and handles invalid inputs.

Conditionals and Logical Operators

• If, elif, and else are used to perform actions based on conditions.
• Comparison operators: >, >=, <, <=, == (equality), != (not equal).
• Logical operators: and, or combine multiple conditions.
• Indentation and colons are required to define code blocks in Python.

Loops and Iteration

• While loops repeat actions until a condition is false; risk of infinite loops.
• For loops iterate over lists, ranges, or items in data structures.
• The range() function generates a sequence of numbers for iteration.
• Break and continue control loop flow.

Data Structures: Lists, Dictionaries, and Sets

• Lists store ordered collections; items accessed by index (starting at 0).
• Dictionaries store key-value pairs for direct access.
• Sets store unique values without duplicates.
• Use list.append(), dict[key]=value, set.add() to add items.

File I/O and CSV Handling

• open(filename, "w" or "a" or "r") creates, appends, or reads files.
• Use with open(...) as file: for automatic file closing.
• The csv module handles reading/writing CSV files; DictReader/DictWriter map headers to data.

Regular Expressions (Regex)

• Regex defines text patterns for matching or extracting information.
• re.search(), re.sub(), and related functions match or replace patterns.
• Common symbols: . (any char), * (0+), + (1+), ? (0/1), ^ (start), $ (end), [] (set), | (or).
• Capture groups (parentheses) extract parts of a match.

Object-Oriented Programming (OOP)

• Classes define custom data types (templates); objects are instances.
• init initializes object attributes; self refers to the instance.
• Attributes (instance variables) store object-specific data.
• Methods are functions inside classes (including special str for representations).
• Properties (decorators @property, @setter) control access and validation.
• Inheritance allows new classes to reuse functionality from existing classes.
• Operator overloading enables custom behavior for operators like +.

Advanced Python Tools & Features

• Unit testing ensures code correctness; pytest automates tests.
• Type hints (e.g., def func(x: int) -> str) annotate variable and return types; mypy checks types.
• Global variables can be modified in functions using the global keyword.
• List and dictionary comprehensions allow concise data processing.
• Generators (yield) produce values one at a time, conserving memory.
• Command-line arguments are parsed using argparse for robust user input.
• Docstrings document functions/classes for automated help and documentation.

Key Terms & Definitions

• Function — Named block that performs actions, can accept arguments and return values.
• Variable — Named storage for data values in a program.
• String (str) — Sequence of text characters.
• Integer (int) — Whole number without decimal point.
• List — Ordered, mutable collection of items.
• Dictionary (dict) — Key-value pairs for fast lookups.
• Set — Unordered collection of unique items.
• Exception — Runtime error that can be caught and handled.
• Class — Blueprint for custom data types in OOP.
• Object — Instance of a class.
• Method — Function defined inside a class.
• Property — Controlled attribute access in classes.
• Inheritance — Reusing and extending functionality of another class.
• Regular Expression (Regex) — Pattern-matching tool for strings.
• Unit Test — Code that tests specific parts of a program for correctness.
• Type Hint — Annotation indicating variable/function expected data type.
• Generator — Function that yields values one at a time.
• Comprehension — Compact syntax to build lists/dicts/sets from iterables.

Action Items / Next Steps

• Complete any assigned problem sets for hands-on practice.
• Explore Python’s official documentation and tutorials for further learning.
• Practice writing and testing your own small projects using discussed concepts.
• Experiment with libraries like argparse, csv, and pytest.
• Apply object-oriented programming concepts to model real-world problems.