Python Programming

Python Ecosystem

Python Ecosystem and Setup

• Python in data science, web development, and automation

• Advantages in data analysis and visualization

• Comparison with other programming languages

• Python 3.x vs. legacy versions

• Community and ecosystem overview

Development Environment Setup

Setup Fundamentals

• Choosing an IDE: VS Code, PyCharm, Jupyter

• For this course (if accessible within the company): Google Colab

• Virtual environment management (venv, conda)

• Package management with pip and conda

Syntax Rules

Basic Syntax and Structure

• Indentation and code blocks

• Comments and documentation strings (docstrings)

• Variable naming conventions

Core Data Structures and Strings

Basic Data Types and Operations

• Core data types: int, float, bool, str

• The concept of variables and variable declaration

• Type checking and type hints

• Dynamic typing concept

• Type casting and conversions

• Operators and expressions (addition, subtraction, modulo, etc.)

• Constants and immutability

String Fundamentals

• Strings as sequences of characters

• String definition methods (single quotes, double quotes, triple quotes)

• String concatenation (+, join)

• String repetition

• Escape characters and raw strings

• Multiline strings and docstrings

Indexing and Slicing

• Positive and negative indexing

• String slicing techniques

• Using the step parameter

• String immutability

• String copying and memory management

String Methods and Formatting

• find(), index(), rfind(), rindex()

• startswith(), endswith()

• upper(), lower(), capitalize(), title(), swapcase()

• strip(), lstrip(), rstrip()

• split() and .join()

• f-strings and advanced formatting features

• Regular Expression fundamentals

Built-in Collection Data Types

Ordered Collection Data Structures

• Lists: creation, indexing, slicing

• List methods and operations

• Constant-time element access mechanics

• Tuples and the benefits of immutability

• When to use list vs. tuple

• Performance considerations

Dictionary and Set Data Types

• Dictionaries: key-value pairs

• Which types can be used as keys

• Dictionary methods and iteration

• Hashing mechanics

• Sets and frozensets

• Set operations: union, intersection, difference

• Choosing the right data structure

• Why element lookup is fast in dictionaries and sets

Control Structures

Comparing Elements

• Comparison operators: greater than, less than, greater than or equal to, etc.

• Logical operators: and, or, not

• Short-circuit evaluation in logical operators

• String comparison operations

Conditional Statements

• if/elif/else statements

• Nested conditional structures

• Ternary operator

• Pattern matching

Loops

The for Loop

• Definite iteration over sequences

• The range() function

The while Loop

• Condition-based loops

• Infinite loops and their use cases

• Loop control variables

• When to use while vs. for

Iterating Over Elements

• Iterating over ordered sequences

• Iterating over strings

• Looping over dictionary keys and values

• Looping over set elements

• Pitfalls of mutating a dictionary or set during iteration

Loop Control Statements

• Using continue and break

• The loop else clause

• Strategies for breaking out of nested loops

Advanced Iteration and Manipulation

Using Built-in Functions

• Indexing and values together with enumerate()

• Multi-sequence iteration with zip()

• Reverse iteration with reversed()

• Sorted iteration with sorted()

• Using iter() and next()

Comprehensions

• List comprehension syntax and patterns

• Dictionary and set comprehensions

• How they impact memory efficiency

• When to use comprehensions vs. loops

• Performance optimization techniques

Function Fundamentals

What Is a Function and Why Use It?

• The concept of abstraction

• Code reusability

• The DRY (Don't Repeat Yourself) principle

• Improving code organization and readability

Defining and Calling Functions

• The def statement

• The return statement

• Void functions

Function Parameters and Arguments

• Positional arguments

• Keyword arguments

• Default arguments

• Combining positional and keyword arguments

• Using *args and **kwargs

Return Values and Scope

Return Mechanics

• Returning a single value

• Returning multiple values with tuples

• Returning None

• return vs. print

• Returning different data types

• Controlling output with conditional statements

• Handling errors in functions

Namespaces and Scope

• Local scope and global scope

• Built-in scope

• Variable lifecycle and garbage collection

• Naming collisions and their consequences

• How Python manages namespaces

Advanced Function Concepts

Lambda Functions

• Anonymous functions

• Defining lambda functions

• When to use regular functions vs. lambda functions

• Using lambdas with built-in functions (map, filter, sorted)

• Lambdas in data processing pipelines

Decorators and Higher-Order Functions

• Functions as first-class objects

• Passing a function as an argument to another function

• Introduction to decorators

• Common decorator patterns

Recursion

• Recursive thinking

• Base case and recursive case

• Benefits and limitations of recursion

• When to use recursion vs. iteration

• Classic recursion examples

Generators

• The yield keyword and generator functions

• Differences between generators and regular functions

• Memory efficiency through lazy evaluation

• Generator state and execution flow

• Using multiple yield statements

• Infinite generators

• Benefits of generators when working with large datasets

OOP Fundamentals

Classes and Objects

• Benefits of the OOP approach

• OOP vs. functional programming

• Concepts of class and object

• Core principles of OOP (Encapsulation, Inheritance, Polymorphism, Abstraction)

• Defining a class

• Instantiating objects from a class

• Variable → Attribute, Function → Method concepts

Class Components

Deep Dive into Class Mechanics

• Instance attributes vs. class attributes

• The __init__ method

• The self parameter

• Using instance methods

• @classmethod and @staticmethod decorators

• Getters, setters, and deleters

Advanced OOP Concepts

Inheritance and Polymorphism

• Parent/child and superclass/subclass concepts

• Single and multiple inheritance

• Effective use of super() and inherited methods

• Method overriding

• Polymorphism

Special / Magic Methods

• String representation: __str__ and __repr__

• Comparison operators: __eq__, __lt__

• Arithmetic operators: __add__, __mul__

• Other dunder/magic methods

Exception Handling

Error Management

• try/except/else/finally blocks

• Common built-in exception types

• Creating custom exceptions

• Exception hierarchies

• Exception handling best practices

Debugging Techniques

• What is debugging?

• Using debugger tools

• Configuring the logging module

• Assertion statements

• Unit testing fundamentals

• Profiling and performance analysis

File I/O

File Operations

• Reading and writing text files

• Binary file processing

• Working with CSV and JSON files

• Context managers for file handling

• Path manipulation with pathlib

Data Serialization

• JSON encoding and decoding

• Pickling Python objects

• Working with databases (sqlite3)

• Processing configuration files

• Data validation strategies

Package Management Fundamentals

Using pip

• What is pip and how it works

• pip install command and parameters

• Viewing installed packages with pip list

• Inspecting package details with pip show

• Removing packages with pip uninstall

• pip freeze and generating requirements.txt

Using conda

• Installing Anaconda/Miniconda and their differences

• conda install command and channels

• Listing packages with conda list

• Updating packages with conda update

• Removing packages with conda remove

• When to use conda vs. pip

Setting Up Virtual Environments

Environments with Python venv

• Why virtual environments are needed and their benefits

• Creating an environment with python -m venv

• Activating a virtual environment (Windows/Mac/Linux)

• Installing packages with pip

• Using requirements.txt

• Exiting with deactivate

Conda Environment Management

• Creating environments with conda create

• Using conda activate/deactivate

• Listing environments with conda env list

• Creating and using environment.yml files

• Exporting and importing conda environments

• Base environment vs. custom environments

Applied Library Usage Example

Hands-On Practice with Pandas

• pip install pandas

• conda install pandas

• Automatic installation of dependencies

• Version checking and upgrading

• Creating DataFrames and Series

• Reading and writing CSV files

• Basic data filtering and selection

• Checking for missing values

• Grouping and aggregation example

• Exporting data to Excel