Python Fundamentals Overview This skill covers the foundational elements of Python programming including syntax, data types, control structures, functions, object-oriented programming, and the standard library. Learning Objectives Write clean, Pythonic code following PEP 8 guidelines Master all Python data structures (lists, tuples, dicts, sets) Understand and implement object-oriented programming concepts Navigate and utilize the Python standard library effectively Manage virtual environments and packages with pip/venv Core Topics 1. Python Syntax & Data Types Variables and naming conventions Numeric types (int, float, complex) Strings and string methods Boolean logic and None Type hints (PEP 484) F-strings and formatting Code Example:
Type hints and f-strings
def greet_user ( name : str , age : int ) -
str : return f"Hello { name } , you are { age } years old!"
Using the function
message
greet_user ( "Alice" , 30 ) print ( message )
Hello Alice, you are 30 years old!
- Control Flow & Functions Conditional statements (if/elif/else) Loops (for, while) List/dict/set comprehensions Function parameters and arguments Lambda functions Decorators basics Code Example:
List comprehension with conditional
numbers
[ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] even_squares = [ x ** 2 for x in numbers if x % 2 == 0 ] print ( even_squares )
[4, 16, 36, 64, 100]
Decorator example
def timing_decorator ( func ) : import time def wrapper ( * args , ** kwargs ) : start = time . time ( ) result = func ( * args , ** kwargs ) end = time . time ( ) print ( f" { func . name } took { end - start : .4f } seconds" ) return result return wrapper @timing_decorator def slow_function ( ) : import time time . sleep ( 1 ) return "Done!" 3. Object-Oriented Programming Classes and instances Inheritance and composition Polymorphism and duck typing Encapsulation (public/private) Magic methods ( init , str , repr , etc.) Abstract base classes Code Example: from abc import ABC , abstractmethod class Vehicle ( ABC ) : def init ( self , brand : str , model : str ) : self . brand = brand self . model = model @abstractmethod def start_engine ( self ) -
str : pass def str ( self ) -
str : return f" { self . brand } { self . model } " class Car ( Vehicle ) : def init ( self , brand : str , model : str , doors : int ) : super ( ) . init ( brand , model ) self . doors = doors def start_engine ( self ) -
str : return f" { self } engine started with { self . doors } doors"
Usage
car
Car ( "Toyota" , "Camry" , 4 ) print ( car . start_engine ( ) )
Toyota Camry engine started with 4 doors
- Standard Library File operations (open, read, write) Path handling with pathlib datetime for date/time operations collections (Counter, defaultdict, namedtuple) itertools and functools json and csv modules Code Example: from pathlib import Path from datetime import datetime , timedelta from collections import Counter import json
Path operations
data_dir
Path ( "data" ) data_dir . mkdir ( exist_ok = True ) config_file = data_dir / "config.json" config = { "app_name" : "MyApp" , "version" : "1.0.0" }
Write JSON
with open ( config_file , "w" ) as f : json . dump ( config , f , indent = 2 )
Read JSON
with open ( config_file , "r" ) as f : loaded_config = json . load ( f )
Date operations
today
datetime . now ( ) next_week = today + timedelta ( days = 7 ) formatted = today . strftime ( "%Y-%m-%d %H:%M:%S" )
Counter for frequency analysis
words
[ "apple" , "banana" , "apple" , "cherry" , "banana" , "apple" ] word_count = Counter ( words ) print ( word_count . most_common ( 2 ) )
[('apple', 3), ('banana', 2)]
Hands-On Practice Project 1: File Management Tool Build a command-line tool that organizes files by extension. Requirements: Accept directory path as input Scan for files recursively Group files by extension Move files to organized folders Generate summary report Key Skills: pathlib, file I/O, os module Project 2: Contact Manager (OOP) Create a contact management system using object-oriented principles. Requirements: Contact class with name, email, phone ContactBook class to manage contacts CRUD operations (Create, Read, Update, Delete) Search and filter functionality Persist data to JSON file Key Skills: OOP, JSON serialization, data structures Project 3: Text Analyzer Analyze text files for statistics and patterns. Requirements: Word frequency analysis Character count (with/without spaces) Average word length Most common words (exclude stop words) Export results to CSV Key Skills: String manipulation, collections.Counter, CSV Assessment Criteria Write PEP 8 compliant Python code Use appropriate data structures for different scenarios Implement classes with proper OOP principles Utilize standard library modules effectively Manage virtual environments and dependencies Handle exceptions properly Write clear docstrings and comments Resources Official Documentation Python.org - Official Python documentation PEP 8 - Python style guide Python Tutorial - Official tutorial Learning Platforms Real Python - Comprehensive tutorials Python for Everybody - Free course Automate the Boring Stuff - Practical Python Tools VS Code - Recommended editor PyCharm - Professional IDE pylint - Code linter black - Code formatter Next Steps After mastering Python fundamentals, proceed to: Django Framework - Build web applications Pandas Data Analysis - Work with data Pytest Testing - Write comprehensive tests Asyncio Programming - Asynchronous Python