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Semester 2: Python Programming
Introduction to Computers, Programs and Python: Computer components, programming languages, Python history, basic programming style and errors
Introduction to Computers, Programs and Python
Computer Components
Computers consist of hardware and software components. Hardware includes the physical parts like the CPU, memory, storage devices, and peripherals. The CPU is the brain of the computer, processing instructions. Memory, like RAM, temporarily stores data for active processes. Storage devices, such as HDDs and SSDs, retain data long-term. Peripherals include input devices (keyboard, mouse) and output devices (monitor, printer).
Programming Languages
Programming languages are formal languages comprising instructions that can be executed by a computer. They can be classified into low-level languages (assembly, machine code) and high-level languages (Python, Java, C++). High-level languages are more user-friendly, allowing programmers to write code using abstract concepts. Each language serves different purposes, with specific applications suited to their syntax and capabilities.
Python History
Python was created by Guido van Rossum and first released in 1991. It was designed with code readability in mind, emphasizing simplicity and ease of use. Over the years, Python has gained popularity due to its versatility in various domains such as web development, data science, artificial intelligence, and automation. Python continues to evolve, with regular updates introducing new features and enhancements.
Basic Programming Style
Writing clean and maintainable code is crucial. Basic programming style includes consistent indentation and naming conventions, the use of comments for documentation, and modular programming practices. Python enforces indentation to define code blocks, promoting readability. Following a consistent style helps in understanding and maintaining code over time.
Errors in Python Programming
Errors can be classified into syntax errors, runtime errors, and logical errors. Syntax errors occur when the code does not conform to the language rules, preventing it from running. Runtime errors happen during execution, often due to invalid operations (like division by zero). Logical errors occur when the code runs without crashing but produces incorrect results. Debugging techniques are essential for identifying and resolving these issues.
Python Basics: Input/output, variables, expressions, numeric data types, operators, strings and objects
Python Basics
Input/Output
Input and output operations in Python are primarily handled using the input() function for user input and the print() function for output. Input can be taken in various formats, and the output can be formatted using f-strings or the format() method.
Variables
Variables in Python are used to store data values. They are created the moment you assign a value to them. Variable names must start with a letter or underscore, followed by letters, numbers, or underscores.
Expressions
An expression is a combination of values, variables, operators, and function calls that results in a value. For example, 5 + 3 is an expression that results in the value 8.
Numeric Data Types
Python has several numeric data types, including integers, floating-point numbers, and complex numbers. Integers are whole numbers, floats are decimal numbers, and complex numbers have a real and imaginary part.
Operators
Python supports a variety of operators such as arithmetic ( +, -, *, /), comparison ( ==, !=, <, >), and logical operators ( and, or, not) which are used to perform computations and comparisons.
Strings
Strings are a sequence of characters and can be defined using single quotes, double quotes, or triple quotes for multi-line strings. Strings are immutable, meaning once defined, they cannot be changed.
Objects
In Python, everything is an object, including functions, modules, and data types. Objects have attributes and methods that allow you to perform operations on them.
Control Structures: If statements, loops, logical operators, conditional expressions
Control Structures
If Statements
If statements are used to make decisions in programming. They allow the execution of a block of code only if a specified condition is true. The syntax generally includes the keyword 'if' followed by a condition in parentheses and a block of code to execute if the condition is met. Optionally, else and elif statements can be added to handle other conditions.
Loops
Loops are control structures that allow repetitive execution of code blocks. The two main types of loops in Python are for loops and while loops. For loops iterate over a sequence (like a list or string), whereas while loops continue executing as long as a specified condition remains true. Loops help in reducing code redundancy and automating repetitive tasks.
Logical Operators
Logical operators are used to combine multiple conditions in control statements. The most common logical operators in Python are 'and', 'or', and 'not'. 'And' returns true if both conditions are true, 'or' returns true if at least one condition is true, and 'not' reverses the truth value of the condition. These operators are crucial for creating complex conditional expressions.
Conditional Expressions
Conditional expressions, also known as ternary operators, allow for a shorthand way to evaluate conditions. The syntax involves a single line that includes conditions and two possible outcomes. It's helpful for succinctly assigning values based on a condition.
Functions: Defining and calling functions, arguments, recursion, modular programming
Functions in Python Programming
Defining Functions
Functions in Python are defined using the def keyword followed by the function name and parentheses containing any parameters. A function can have any number of parameters, including none. The function body contains the code that will be executed when the function is called.
Calling Functions
To execute a function, you simply call it by its name followed by parentheses. If the function takes parameters, you need to provide the appropriate arguments within the parentheses. Functions can be called multiple times throughout a program.
Arguments
Arguments are the values that we pass into a function when we call it. Python supports several types of arguments, including positional arguments, keyword arguments, and default arguments. Positional arguments are passed in the order defined, while keyword arguments are passed by explicitly stating the parameter name.
Recursion
Recursion is a programming technique where a function calls itself to solve a problem. A recursive function must have a base case to prevent infinite recursion. Recursive functions are often used for tasks such as calculating factorials or traversing tree structures.
Modular Programming
Modular programming involves breaking down a program into smaller, manageable, and reusable functions or modules. This approach facilitates code reuse, improves maintainability, and makes debugging easier. Each module can be developed and tested independently before integrating into the larger program.
Object Oriented Programming: Classes and objects, inheritance, polymorphism, encapsulation
Object Oriented Programming
Classes and Objects
Classes are blueprints for creating objects. An object is an instance of a class. In Python, a class can encapsulate data for the object and methods to manipulate that data. Objects have attributes (data) and behaviors (methods). Example: Class Dog with attributes like breed and methods like bark.
Inheritance
Inheritance allows a class (child class) to inherit attributes and methods from another class (parent class). This promotes code reusability. In Python, inheritance is implemented by defining a child class that takes the parent class as a parameter. Example: Class Animal is a parent class; Class Dog inherits from Animal.
Polymorphism
Polymorphism allows methods to do different things based on the object it is acting upon. In Python, this can be achieved through method overriding where a child class provides a specific implementation of a method already defined in its parent class. Example: A method 'make_sound' can be implemented differently in classes Dog and Cat.
Encapsulation
Encapsulation is the bundling of data and methods that operate on that data within a single unit, usually a class. It restricts direct access to some of the object's components, which can prevent the accidental modification of data. In Python, encapsulation is implemented using private and public access modifiers.
Data Structures in Python: Strings, lists, tuples, sets, dictionaries, multidimensional lists
Data Structures in Python
Strings
Strings in Python are sequences of characters. They are immutable, meaning once created, they cannot be changed. Strings can be defined using single quotes or double quotes. Python provides various methods for string manipulation like concatenation, slicing, and formatting. Common operations include finding length, changing case, and searching for substrings.
Lists
Lists are ordered collections of items that can store mixed data types. They are mutable, allowing modifications such as adding, removing, or changing items. Lists are defined using square brackets. Key methods include append, extend, insert, remove, and pop. Lists support indexing and slicing, providing flexibility in data handling.
Tuples
Tuples are similar to lists but are immutable. They are defined using parentheses. Tuples can hold mixed data types and provide a way to store related data. Unlike lists, once a tuple is created, it cannot be altered. They are often used for fixed collections of items, like coordinates.
Sets
Sets are unordered collections of unique items. They eliminate duplicate entries and are defined using curly braces or the set() function. Sets are mutable; however, they cannot store mutable elements like lists or dictionaries. Common operations include union, intersection, and difference.
Dictionaries
Dictionaries are collections of key-value pairs. They are unordered and mutable, with keys being unique. Defined using curly braces with key-value pairs, dictionaries allow quick data retrieval. Methods include get, keys, values, and items, providing efficient data management for associative data.
Multidimensional Lists
Multidimensional lists, or lists of lists, allow the creation of complex data structures like matrices. They can be used to represent tables or grids. Accessing elements requires multiple indexes. They are flexible and support various operations like iteration and slicing.
File Handling and Exception Handling: Reading/writing text and binary files, exception handling, custom exceptions
File Handling and Exception Handling
Introduction to File Handling
File handling refers to the process of reading from and writing to files. In Python, files can be handled using built-in functions that provide capabilities to work with both text and binary files.
Reading Text Files
To read a text file, the open function is used with the mode 'r'. Common methods include read(), readline(), and readlines(). It is essential to close the file after reading to free up resources.
Writing to Text Files
Writing to text files can be accomplished using open function with mode 'w' or 'a'. The write() method writes a string to the file, and writelines() can be used for a list of strings.
Reading Binary Files
Binary files are read using mode 'rb'. Data read is in bytes, and methods like read() can be used. Proper handling of the data format is crucial when processing binary files.
Writing to Binary Files
To write binary files, the mode 'wb' is used. The write() method can be utilized to write byte data to the file, ensuring that data types are handled appropriately.
Exception Handling
Exception handling is crucial for managing errors that occur during file operations. The try-except block is commonly used to catch and handle exceptions.
Common Exceptions in File Handling
Common exceptions include FileNotFoundError, IOError, and PermissionError. Each exception can be handled specifically to manage program flow effectively.
Creating Custom Exceptions
Custom exceptions can be defined by subclassing the Exception class. They allow for more granular control over error handling specific to applications.
Best Practices in File Handling and Exception Handling
Best practices include using 'with' statements for file operations, validating file paths, and ensuring data is appropriately encoded and decoded when working with text and binary files.
