Programming in Python is not just about writing code; it’s also about understanding the language’s vocabulary. Knowing the key terms and concepts can significantly enhance your coding efficiency and problem-solving skills. Here, we outline the essential 100 Python vocabulary words that every programmer should know.
1.Variable: A container that holds a value.
2.Function: A block of code designed to perform a specific task.
3.Class: A blueprint for creating objects with predefined attributes and methods.
4.Object: An instance of a class.
5.Method: A function defined inside a class.
6.String: A sequence of characters.
7.List: A collection of items that can be accessed by index.
8.Tuple: An immutable list.
9.Set: A collection of unique items.
10.Dictionary: A collection of key-value pairs.
11.Loop: A programming structure that repeats a block of code until a specific condition is met.
12.Conditional Statement: A programming structure that executes code based on certain conditions.
13.Boolean: A data type that can be either True or False.
14.Indentation: The spaces or tabs at the beginning of a line of code, crucial for Python syntax.
15.Module: A file containing Python definitions and statements.
16.Package: A collection of modules.
17.Import: A statement that allows access to functionalities from other modules or packages.
18.Exception: An event that occurs during the execution of a program and disrupts its normal flow.
19.Try-Except: A block used to handle exceptions.
20.Lambda: A small anonymous function.
21-30:
–Comprehension: A concise way to create lists, sets, or dictionaries.
–Generator: A simple and powerful tool for creating iterators.
–Decorator: A function that modifies the functionality of another function.
–Inheritance: The capability of one class to derive or inherit the properties from another class.
–Encapsulation: The bundling of data with the methods that operate on that data.
–Polymorphism: The ability of an object to take on many forms.
–Mutable: Data that can be modified after it’s created.
–Immutable: Data that cannot be modified after it’s created.
–Asynchronous: Code execution that occurs independently of other code.
–Synchronous: Code execution that occurs in a sequential manner.
31-40:
–API: Application Programming Interface, a set of routines, protocols, and tools for building software applications.
–IDE: Integrated Development Environment, a software application that provides comprehensive facilities to computer programmers for software development.
–PIP: The package installer for Python.
–Virtual Environment: An isolated environment for Python projects.
–Data Structure: A way of organizing data in a computer so that it can be used efficiently.
–Algorithm: A process or set of rules to be followed in calculations or other problem-solving operations.
–Syntax: The set of rules that defines the combinations of symbols that are considered to be correctly structured expressions in that language.
–Semantic: The meaning of a piece of code.
–Debugging: The process of finding and resolving errors in computer programs.
–Optimization: The process of improving the efficiency or performance of a program.
41-50:
–Recursion: A method where the solution to a problem depends on solutions to smaller instances of the same problem.
–Heap: A special tree-based data structure where the tree is a complete binary tree.
–Stack: A data structure that follows a particular order in which the operations are performed.
–Queue: A linear structure that follows a particular order in which the operations are performed. The order is First In First Out (FIFO).
–Linked List: A sequence of nodes that are connected together.
–Tree: A widely used abstract data type that simulates a hierarchical tree structure with a root value and subtrees of children.
–Graph: A non-linear data structure that can be looked at as a collection of vertices potentially connected by line segments named edges.
–Hash Table: A data structure that uses a hash function to compute an index into an array of buckets or slots, from which the desired value can be found.
–Big O Notation: A methodology used to determine how quickly an algorithm grows or declines in performance as the input size increases.
–Concurrency: The execution of multiple tasks or processes
