1. Python:
Python is a high-level, interpreted programming language known for its simplicity and readability.
2. Interpreter:
A program that translates and executes Python code line by line.
3. Variable:
A named storage location used to store data in Python. Variables are created using the assignment operator (=) and can hold different data types.
4. Data Type:
A classification of data in Python, such as integer, float, string, list, tuple, dictionary, etc.
5. String:
A sequence of characters enclosed within single (''), double (" "), or triple (''' ''') quotes. Strings can be manipulated using various string methods.
6. Integer:
A data type that represents whole numbers without any fractional part.
7. Float:
A data type that represents real numbers with a decimal point.
8. Boolean:
A data type that represents True or False values.
9. List:
A mutable, ordered collection of items separated by commas and enclosed within square brackets ([]). Lists can contain elements of different data types.
10. Tuple:
An immutable, ordered collection of items separated by commas and enclosed within parentheses (()). Tuples are similar to lists but cannot be modified after creation.
11. Dictionary:
A collection of key-value pairs enclosed within curly braces ({}). Each key is associated with a value, and keys must be unique within a dictionary.
12. Set:
A collection of unique elements enclosed within curly braces ({}). Sets do not allow duplicate values and are unordered.
13. Function:
A block of reusable code that performs a specific task when called. Functions can accept input parameters (arguments) and return values.
14. Module:
A file containing Python code that can be imported and used in other Python programs. Modules help organize code and facilitate code reuse.
15. Package:
A collection of Python modules stored in directories and subdirectories. Packages allow for the organization and distribution of related modules.
16. Import:
The process of including a module or package in a Python program using the `import` statement. Imported modules can be accessed and used within the program.
17. Conditional Statement:
A control structure that executes code based on the evaluation of a condition. Common conditional statements in Python include if, elif, and else.
18. Loop:
A control structure that repeats a block of code until a specified condition is met. Python supports for loops, while loops, and nested loops.
19. Indentation:
The whitespace at the beginning of a line used to indicate blocks of code in Python. Indentation is crucial for code readability and is used to define the scope of code blocks.
20. Slice:
A portion of a sequence (e.g., string, list, tuple) extracted using the slicing operator ([]). Slicing allows for the extraction of sub-sequences from larger sequences.
21. Indexing:
Accessing individual elements of a sequence using their position within the sequence. Indexing in Python starts from 0 for the first element.
22. Mutable:
A data type that can be modified after creation. Lists, dictionaries, and sets are mutable data types in Python.
23. Immutable:
A data type that cannot be modified after creation. Integers, floats, strings, and tuples are immutable data types in Python.
24. Method:
A function defined within a class that operates on its instance data. Methods are called on objects of the class using dot notation.
25. Instance:
A specific realization of a class that represents a distinct object with its own unique data. Instances are created using the class constructor.
26. Class:
A blueprint for creating objects that defines attributes and methods common to all instances of the class. Classes facilitate code organization and enable object-oriented programming.
27. Encapsulation:
The bundling of data and methods that operate on the data within a single unit (class) to restrict access from outside. Encapsulation helps maintain data integrity and promotes code organization.
28. Polymorphism:
The ability of objects of different classes to respond to the same method invocation in different ways. Polymorphism allows for flexibility and code reuse in object-oriented programming.
29. Overloading:
Defining multiple functions with the same name but different parameter lists within a class. Overloading enables different behaviors for functions based on the number or types of arguments passed to them.
30. Argument:
A value passed to a function when calling it. Arguments provide input data for the function to operate on.
31. Keyword:
A reserved word in Python that has a special meaning and predefined functionality. Keywords cannot be used as variable names or identifiers.
32. Lambda Function:
An anonymous function defined using the `lambda` keyword. Lambda functions are small, one-line functions used for simple operations.
33. Generator:
A function that returns an iterator and generates values lazily. Generators conserve memory and improve performance by yielding values one at a time.
34. Decorator:
A function that takes another function as input and extends or modifies its behavior without changing its source code. Decorators are commonly used for adding functionality to functions.
35. Exception:
An error that occurs during the execution of a Python program. Exceptions are raised when unexpected conditions occur, and they can be handled using try-except blocks.
36. Iterable:
An object capable of returning its members one at a time, used in loops or comprehensions.
37. Iterator:
An object that represents a stream of data and supports iteration through its elements using the next() function.
38. Yield:
A keyword used in generator functions to return a value and temporarily suspend the function's execution.
39. Mutable Sequence:
A data type in Python that represents an ordered collection of items that can be modified after creation, such as lists.
40. Immutable Sequence:
A data type in Python that represents an ordered collection of items that cannot be modified after creation, such as tuples.
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