Data Structures in Python

Study Notes

Lists:
- Ordered collection of items
- Can be modified (mutable)
- Indexing starts at 0
- Supports slicing, concatenation, and indexing
Tuples:
- Ordered, immutable collection of items
- Faster than lists, but cannot be modified
- Useful for storing data that shouldn't change
Dictionaries:
- Unordered collection of key-value pairs
- Keys must be unique, immutable, and hashable
- Values can be of any type, including mutable
Sets:
- Unordered collection of unique items
- Fast membership testing and insertion
- Supports union, intersection, and difference operations

Classes:
- Define a blueprint for creating objects
- Contain attributes (data) and methods (functions)
- Can inherit behavior from parent classes
Objects:
- Instances of classes
- Have their own attributes and methods
- Can be manipulated independently
Inheritance:
- Mechanism for creating a new class from an existing class
- Child class inherits attributes and methods from parent class
- Can also override or extend parent class behavior
Polymorphism:
- Ability of an object to take on multiple forms
- Can be achieved through method overriding or method overloading

Flask and Django:
- Two popular web frameworks for building web applications
- Flask is a microframework, Django is a full-featured framework
Requests and Responses:
- HTTP requests: GET, POST, PUT, DELETE, etc.
- HTTP responses: status codes, headers, and bodies
Templates:
- Use template engines like Jinja2 or Mustache to render dynamic content
- Separate presentation logic from application logic
Databases:
- Use databases like MySQL, PostgreSQL, or SQLite to store data
- Interact with databases using ORM (Object-Relational Mapping) tools

NumPy:
- Library for efficient numerical computation
- Supports arrays, matrices, and mathematical operations
Pandas:
- Library for data manipulation and analysis
- Supports data structures like Series (1D) and DataFrames (2D)
- Provides data filtering, grouping, and merging capabilities
Matplotlib and Seaborn:
- Data visualization libraries
- Create plots, charts, and graphs to visualize data
Scikit-learn:
- Library for machine learning
- Provides algorithms for classification, regression, clustering, and more
- Supports data preprocessing, feature selection, and model evaluation

Lists:
- Ordered collection of items that can be modified
- Indexing starts at 0, supports slicing, concatenation, and indexing
Tuples:
- Ordered, immutable collection of items, faster than lists
- Useful for storing data that shouldn't change, cannot be modified
Dictionaries:
- Unordered collection of key-value pairs, keys must be unique, immutable, and hashable
- Values can be of any type, including mutable
Sets:
- Unordered collection of unique items, fast membership testing and insertion
- Supports union, intersection, and difference operations

Classes:
- Define a blueprint for creating objects, contain attributes (data) and methods (functions)
- Can inherit behavior from parent classes
Objects:
- Instances of classes, have their own attributes and methods
- Can be manipulated independently
Inheritance:
- Mechanism for creating a new class from an existing class
- Child class inherits attributes and methods from parent class, can also override or extend parent class behavior
Polymorphism:
- Ability of an object to take on multiple forms, can be achieved through method overriding or method overloading

Flask and Django:
- Two popular web frameworks for building web applications, Flask is a microframework, Django is a full-featured framework
Requests and Responses:
- HTTP requests: GET, POST, PUT, DELETE, etc.
- HTTP responses: status codes, headers, and bodies
Templates:
- Use template engines like Jinja2 or Mustache to render dynamic content
- Separate presentation logic from application logic
Databases:
- Use databases like MySQL, PostgreSQL, or SQLite to store data
- Interact with databases using ORM (Object-Relational Mapping) tools

NumPy:
- Library for efficient numerical computation
- Supports arrays, matrices, and mathematical operations
Pandas:
- Library for data manipulation and analysis
- Supports data structures like Series (1D) and DataFrames (2D)
- Provides data filtering, grouping, and merging capabilities
Matplotlib and Seaborn:
- Data visualization libraries, create plots, charts, and graphs to visualize data
Scikit-learn:
- Library for machine learning
- Provides algorithms for classification, regression, clustering, and more
- Supports data preprocessing, feature selection, and model evaluation