Cheat Sheet for Data Structures in Python

This cheat sheet provides a concise overview of common data structures used in Python programming, with a focus on Open Source Intelligence (OSINT).

Data Structures

• Stack: A Last-In-First-Out (LIFO) data structure that follows the principle "last element inserted is the first one to be removed".
• Queue: A First-In-First-Out (FIFO) data structure that follows the principle "first element inserted is the first one to be removed".
• Tree: A hierarchical data structure consisting of nodes with a value and zero or more child nodes.
• Graph: An unordered collection of vertices connected by edges, which represent relationships between them.
• Hash Table: An associative data structure that stores key-value pairs in an array using a hash function to map keys to indices.

OSINT Data Structures

• Entity Extraction: The process of extracting relevant information from unstructured text, such as names, dates, and locations.
• Named Entity Recognition (NER): A subfield of entity extraction that focuses on identifying named entities like people, organizations, and locations.
• Text Preprocessing: The process of cleaning and normalizing text data to prepare it for analysis, including tokenization, stemming, and lemmatization.
• Topic Modeling: A technique used to discover hidden topics or themes in a large corpus of text data.

Python Implementations

• List: Python's built-in list data structure, which is implemented as a dynamic array.
• Trie: A data structure used for efficient string matching and auto-completion, implemented using a dictionary in Python.
• Heap: A specialized tree-based data structure that satisfies the heap property, implemented using an array in Python.

Common Methods

• append(): Adds an element to the end of a list or queue.
• pop(): Removes and returns the last element from a stack or queue.
• insert(): Inserts an element at a specific position in a list or set.
• remove(): Removes the first occurrence of an element in a list.

Best Practices

• Use meaningful variable names and comments to improve code readability.
• Follow PEP 8 guidelines for coding style and conventions.
• Use Python's built-in data structures whenever possible, unless a specific use case requires otherwise.

This cheat sheet provides a solid foundation for working with data structures in Python. Remember to practice implementing these concepts to improve your skills.