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Python

By reading these notes, you will gain knowledge tailored to your role, whether you are a beginner or practitioner focusing on Python Programming foundations.

> Note – This page contains a complete summary of the topics. To learn more about each topic, click on the (***Expan******d**)*****, Topic-Heading, Image, or ‘Click Here’ hyperlink**.

Before reading this document, please read Programming Basics & Foundations

You can practice Python online https://pythontutor.com/python-compiler.html

Python is high level, interpreted, object oriented, high-level programming language with dynamic semantics. It is used everywhere: Scripting, Application development, websites to Artificial Intelligence and Data Science.

Python Foundation​

TopicKey ConceptsNotesOfficial Documentation LinkPython HistoryVersions of Python Types of PythonThe Origins: Started as a 1989 Christmas hobby project by Guido van Rossum to create a language with ABC's clean syntax and C's power. The Name: Named after the British comedy show Monty Python’s Flying Circus, not the snake. Python 1.0: The 1994 milestone that introduced functional tools like lambda, map, and filter. Python 2.0: The 2000 release that added list comprehensions and shifted to a community-driven open-source model. Python 3.0: A major 2008 overhaul that fixed deep design flaws but was famous for not being backward compatible with Python 2. The "2 vs 3" Era: A 12-year transition period where both versions coexisted until Python 2 officially reached "End of Life" in 2020. Modern Era: Transitioned from a "Benevolent Dictator" (Guido) to a Steering Council, focusing on annual speed and security boosts. CPython: The standard, most popular version of Python written in C it’s what you likely have installed right now. PyPy: A high-speed version that uses a "Just-In-Time" compiler to run pure Python code much faster than the standard version. Jython/IronPython: Specialized versions that allow Python to run directly inside Java (JVM) or Windows (.NET) environments. MicroPython: A tiny, "shrunken" version of Python designed specifically to run on small hardware like Arduinos and sensors.History and LicensePython BasicsVariables, Memory, Data TypesVariables: Think of these as "name tags" or labels attached to data stored in memory, rather than boxes that hold the data themselves. Naming Rules: Variable names must start with a letter or underscore, cannot use special symbols (except _), and are strictly case-sensitive. Naming Conventions: Professional Python code uses snake_case for variables, PascalCase for Classes, and UPPER_CASE for constants (PEP 8). Assigning Values: Python uses "Dynamic Typing," meaning you can reassign a variable from a number to a string without declaring its type first. Input Handling: The input() function always captures data as a String, so you must manually convert it if you need a number or boolean. Outputting Variables: F-strings (f"Hello {name}") are the modern industry standard for printing because they are fast, readable, and clean. Global vs. Local: Local variables live only inside a function, while Global variables are accessible to the whole script but should be used sparingly. Memory Management: Python uses "Reference Counting" to track data and a "Garbage Collector" to automatically delete data when no tags point to it. Data Types: Python categorizes data into types like int, str, and list, which determine how the computer handles and stores that specific information. Mutable-vs.-Immutable: "Mutable" objects (like lists) can be changed in place, while "Immutable" objects (like strings) create a whole new object if modified. Type Casting: The manual process of forcing data from one "currency" to another, such as converting the string "8080" into a mathematical integer. Dynamic Typing: A flexible system where the "type" is attached to the value itself, not the variable name, allowing for rapid and adaptive coding. Virtual Environments (venv): Isolated "digital kitchens" that keep each project's libraries separate to prevent version conflicts and system crashes. Error Handling: Using try and except blocks allows your script to catch "Exceptions" (crashes) and handle them gracefully instead of stopping. Comments & Docstrings: Use # for quick "why" notes and """Triple Quotes""" for professional documentation that explains what your functions do.Python TutorialData TypesNumeric Data TypesIntegers - whole numbers that grow automatically to fit any size float - Numbers with decimals used for measuring things, though they are "approximations" complex - Numbers with a real part and an imaginary j part, used like 2D coordinates for advanced engineering and physics. Booleans - Simple True or False switches that Python actually treats as the numbers 1 and 0 under the hood. NoneType - A unique placeholder that represents "nothing" or "no value," used to signal that data is missing or a task failed. Type-Casting - The process of manually changing data from one type to another, like turning the string "8080" into the integer 8080. Mathematical-Functions - Built-in toolboxes (like the math module) that handle complex calculations like square roots, rounding, and trigonometry instantly.OperatorsArithmetic, Logical, Comparison• // is floor division (drops decimal). • ** is power ($2^3$). • is compares memory identity, == compares value. • Code: if x > 5 and y < 10:OperatorsControl FlowConditionals, Loops• Indentation is mandatory. • range(start, stop, step). • break exits loop; continue skips to next iteration. • Code: for i in range(5): print(i)Control Flow**Strings (Advanced)**Slicing, f-strings, Methods• Strings are immutable. • Slicing: text[start:stop:step] → text[::-1] reverses string. • f-string: f"Hello {name}" is faster/cleaner. • strip(), split(), join().String MethodsData Structures 1Lists & Tuples• List: Mutable []. methods: append, pop, sort. • Tuple: Immutable (). Faster, keys for dicts. • Code: my_list = [1, 2, 3]; my_tuple = (1, 2)Data StructuresData Structures 2Dictionaries & Sets• Dict: Key-Value {k:v}. O(1) lookup speed. • Set: Unique items {1, 2}. Great for removing duplicates. • Code: data.get("key", "default_val") avoids errors.Dictionaries

Functional and Modular Programming​

TopicKey ConceptsNote**sOfficial Documentation Link**FunctionsScope, Return, Args• def keyword. • *args: Tuple of unknown args. • **kwargs: Dict of unknown keyword args. • Docstrings: """Description""" for documentation. • Code: def add(*nums): return sum(nums)Defining FunctionsFunctional ToolsLambda, Map, Filter• Lambda: One-line anonymous function. • Map: Apply function to all items. • Filter: Select items based on condition. • Code: squares = list(map(lambda x: x**2, numbers))Functional HOWTOComprehensionsList/Dict Comprehension• Pythonic way to create lists. • More readable than for loops for simple logic. • Code: [x for x in range(10) if x % 2 == 0]ComprehensionsModules & PackagesImports, __name__• Module: A .py file. • Package: A folder with __init__.py. • if __name__ == "__main__": ensures code runs only when executed directly, not when imported.ModulesVirtual EnvironmentsIsolation, Dependency Mgmt• Crucial for every project. • Prevents library version conflicts. • Cmds: python -m venv venv, source venv/bin/activate (Linux), pip install -r requirements.txt.venv

Object Oriented Programming Python (OOP)​

TopicKey ConceptsNote**sOfficial Documentation Link**Classes & ObjectsBasics, self• Class: Blueprint. Object: Instance. • self: Reference to the current instance. • __init__: Constructor method. • Code: class Dog: def __init__(self, name): self.name = nameClassesInheritanceParent/Child, super()• Don't repeat code. Child inherits Parent's methods. • super().__init__() calls parent constructor. • Polymorphism: Different classes, same method name.InheritanceMagic MethodsDunder Methods• Methods starting/ending with __. • __str__: User-friendly string representation. • __repr__: Developer debugging representation. • __len__, __add__.Data ModelAdvanced OOPDecorators, Properties• @property: Access methods like attributes (Getters). • @staticmethod: Function inside class not using self. • @classmethod: Uses cls (affects class state).Built-in Functions

Advanced Concepts​

TopicKey ConceptsNote**sOfficial Documentation Link**Error HandlingTry, Except, Finally• Never use bare except:. Catch specific errors. • finally: Runs cleanup code (closing files) always. • raise: Trigger custom errors. • Code: try: x/0 except ZeroDivisionError: print("Error")ErrorsFile HandlingRead/Write, Context Mgr• Always use with open(...) to auto-close files. • Modes: 'r' (read), 'w' (write/overwrite), 'a' (append). • Handling CSV and JSON is vital for data exchange.Input/OutputIterators & Generatorsyield, Memory Efficiency• Generator: Returns an iterator, pauses execution with yield. • Extremely memory efficient for large datasets. • Code: def gen(): yield 1; yield 2GeneratorsDecoratorsWrappers, @syntax• Functions that modify other functions without changing their code. • Used for logging, authentication, timing. • Code: @login_required before a function definition.DecoratorsConcurrencyThreading vs AsyncIO• Threading: I/O bound tasks (network calls). • Multiprocessing: CPU bound tasks (calculations). • AsyncIO: Modern async/await for high-performance I/O. • Code: async def main(): await func()AsyncIO

Libraries and Applications​

TopicKey ConceptsNote**sOfficial Documentation Link**Standard Libraryos, sys, datetime• os: File paths, env variables (os.getenv). • sys: System arguments (sys.argv). • datetime: Date manipulation (datetime.now()).Std LibRegexPattern Matching• re module. • Find patterns (emails, phone numbers) in text. • \d (digit), \w (word char), + (one or more).RegexWeb DevelopmentFlask/FastAPI• Flask: Micro-framework, easy for beginners. • Django: Full-stack, "batteries included". • FastAPI: Modern, fast, for building APIs.FlaskData SciencePandas, NumPy• NumPy: Math & Matrix calculations. • Pandas: DataFrames (Excel for Python). • Matplotlib: Graphing & plotting.Pandas

Professional Skills​

TopicKey ConceptsNote**sOfficial Documentation Link**Logginglogging vs print• NEW TOPIC • print is for console; logging is for production. • Levels: DEBUG, INFO, WARNING, ERROR, CRITICAL. • Can save logs to file with timestamps.Logging HOWTOAPI Interactionrequests, JSON• NEW TOPIC • Interacting with REST APIs. • r = requests.get(url). • data = r.json(). handling status codes (200, 404).Requests LibDatabase BasicsSQL, SQLite• NEW TOPIC • Storing data permanently. • Python has built-in sqlite3. • Basic syntax: SELECT, INSERT, CURSOR.SQLite3TestingUnit Tests, TDD• unittest (built-in) or pytest (popular external). • Writing assertions to verify code works. • assert sum([1,2]) == 3.UnittestSecurity Best PracticesSafety, Validation• NEW TOPIC (For DevSecOps) • Never use eval() or exec() on user input. • Use secrets module instead of random for passwords. • SQL Injection prevention (use parameterized queries).Security GuideCode QualityPEP8, Linting• NEW TOPIC • Style guide for Python Code. • Tools: flake8, black (formatter), mypy (type checking). • Type Hints: def add(x: int) -> int:PEP 8