Python Programming Language Basics

Python Programming Language

• Developed in the early 1990s by Guido van Rossum
• Python 3 is the future, and we will be using it in this course
• General-purpose, high-level language that is readable
• Important resource: Python documentation at <a href="https://docs.python.org/3/">https://docs.python.org/3/</a>
• Fun fact: The name "Python" comes from Guido van Rossum's favorite TV show "Monty Python's Flying Circus"

Getting Started

• Download and install Python from <a href="https://www.python.org/downloads/">https://www.python.org/downloads/</a>
• We will be using IDLE as the integrated development environment in this course
• First task: download, install, and write your first Python program (refer to TK2053_Python_Lab0)

Python Interpreter

• Python is an interpreted language
• Two ways to run a Python program:
  • Interactive interpreter: execute one line of code at a time
  • Python file (.py file): store Python programs in text files and run them

Some Fundamentals

• Whitespace is significant in Python
• Python uses indentation to denote code blocks (unlike other languages that use {} or ())
• Comments:
  • Single-line comments denoted by #
  • Multi-line comments begin and end with three "
  • Typically used for documentation

Objects

• An object is an entity with a type (e.g., string or integer)
• The object's type determines what can be done with the data and whether it's mutable (can be changed) or immutable (constant)
• Objects are dynamically typed, meaning Python keeps track of types automatically without requiring declaration code
• Objects are strongly typed, meaning the type of an object doesn't change, even if its value is mutable

Mutable vs Immutable

• Mutable objects are like open boxes, where the value can be changed
• Immutable objects are like closed boxes, where the value can't be changed, but a new object is created when the value changes
• Examples of mutable objects: lists
• Examples of immutable objects: integers, strings, tuples

Lists

• A list is an ordered sequence of Python objects
• Lists can contain objects of any type, including mixed types
• Lists are mutable, meaning they can be modified
• Lists can be created with zero or more elements, separated by commas, and can be converted from other data types
• Lists can be modified by accessing and changing individual items using offsets
• Lists can be sliced to extract a subsequence
• Lists can be nested to contain other lists

Tuples

• Tuples are sequences of arbitrary items, similar to lists
• Tuples are immutable, meaning they can't be added, deleted, or changed after creation
• Tuples can be created with or without parentheses, using commas to separate elements
• Tuples can be used in place of lists, but have fewer functions and use less space
• Tuples can be used as dictionary keys

Dictionaries

• Dictionaries are mutable collections of key-value pairs
• Dictionaries are unordered, meaning the order of items doesn't matter
• Dictionary keys must be unique and immutable (e.g., strings, integers, tuples)
• Dictionary values can be any type
• Dictionaries can be created using curly brackets and key-value pairs
• Dictionaries can be modified by adding, changing, or deleting key-value pairs
• Dictionaries can be used to get values by specifying a key
• Dictionaries have functions to get all keys, values, or key-value pairs

Sets

• Sets are collections of unique values, similar to dictionaries without values
• Sets are mutable, but the keys must be unique and immutable
• Sets can be created using the set() function or curly brackets with comma-separated values
• Sets can be used to test for values, and have operators for union, intersection, and difference
• Sets can be used to discard duplicates from lists, strings, tuples, or dictionaries

Comparing Data Structures

• Lists, tuples, and dictionaries are compared in terms of creating and accessing elements
• Each data structure has its own use cases and advantages

Bigger Data Structures

• Lists, tuples, and dictionaries can be combined to create more complex data structures
• Examples include lists or tuples of lists, or dictionaries of lists
• These data structures can be used to store and manipulate larger amounts of data

Objects

• An object is an entity with a type (e.g., string or integer)
• The object's type determines what can be done with the data and whether it's mutable (can be changed) or immutable (constant)
• Objects are dynamically typed, meaning Python keeps track of types automatically without requiring declaration code
• Objects are strongly typed, meaning the type of an object doesn't change, even if its value is mutable

Mutable vs Immutable

• Mutable objects are like open boxes, where the value can be changed
• Immutable objects are like closed boxes, where the value can't be changed, but a new object is created when the value changes
• Examples of mutable objects: lists
• Examples of immutable objects: integers, strings, tuples

Lists

• A list is an ordered sequence of Python objects
• Lists can contain objects of any type, including mixed types
• Lists are mutable, meaning they can be modified
• Lists can be created with zero or more elements, separated by commas, and can be converted from other data types
• Lists can be modified by accessing and changing individual items using offsets
• Lists can be sliced to extract a subsequence
• Lists can be nested to contain other lists

Tuples

• Tuples are sequences of arbitrary items, similar to lists
• Tuples are immutable, meaning they can't be added, deleted, or changed after creation
• Tuples can be created with or without parentheses, using commas to separate elements
• Tuples can be used in place of lists, but have fewer functions and use less space
• Tuples can be used as dictionary keys

Dictionaries

• Dictionaries are mutable collections of key-value pairs
• Dictionaries are unordered, meaning the order of items doesn't matter
• Dictionary keys must be unique and immutable (e.g., strings, integers, tuples)
• Dictionary values can be any type
• Dictionaries can be created using curly brackets and key-value pairs
• Dictionaries can be modified by adding, changing, or deleting key-value pairs
• Dictionaries can be used to get values by specifying a key
• Dictionaries have functions to get all keys, values, or key-value pairs

Sets

• Sets are collections of unique values, similar to dictionaries without values
• Sets are mutable, but the keys must be unique and immutable
• Sets can be created using the set() function or curly brackets with comma-separated values
• Sets can be used to test for values, and have operators for union, intersection, and difference
• Sets can be used to discard duplicates from lists, strings, tuples, or dictionaries

Comparing Data Structures

• Lists, tuples, and dictionaries are compared in terms of creating and accessing elements
• Each data structure has its own use cases and advantages

Bigger Data Structures

• Lists, tuples, and dictionaries can be combined to create more complex data structures
• Examples include lists or tuples of lists, or dictionaries of lists
• These data structures can be used to store and manipulate larger amounts of data

Learn the basics of Python programming language, its history, and features. This quiz also covers the differences between Python 2 and Python 3.