Exception Handling in Python 3 - try, except, else, finally

Learn how to install Python on Windows using the installer, verify the installation, and set the PATH environment variable to run Python commands from any command prompt.

Learn to write and run Python code using IDLE and the Python shell, create and save .py files, and execute simple programs with print, loops, and basic input.

Write Python code in notepad to read two numbers from the keyboard using input and typecasting, print their sum and product, and run it from the command prompt.

install EditPlus from editplus.com, download the appropriate version, then write and run your first Python program from the command prompt, leveraging syntax highlighting and automatic indentation.

Explore Python identifiers by learning allowed characters, case sensitivity, and common naming conventions, including underscores, protected and private names, and magic variables.

Explore Python reserved words and keywords, learn that booleans use uppercase True, False, None, and see how to view all keywords with the keyword module's kwlist.

Explore Python data types and dynamic typing, contrast with static languages, and learn built-in types like int, float, boolean, string, list, set, dict, and complex using type and print.

Explore the int data type as Python's integral numbers, covering decimal, binary, octal, and hexadecimal forms, with prefixes like 0b and 0x and Python 3 differences from Python 2.

Learn Python’s base conversion functions—bin, oct, and hex—and how to convert integral numbers between bases, generate binary representations, and interpret hex and octal values.

Explore the float data type in Python by distinguishing floating point values from integers, explaining decimal and scientific notation, and how binary representations affect floating numbers.

Explore how Python represents complex numbers with real and imaginary parts using j notation, including syntax like 10+1j, and perform operations on complex data types for scientific and engineering applications.

Explore the bool data type in Python, its true and false values, capitalization of True/False, and how booleans participate in logical and arithmetic operations.

Explore how Python represents strings using single, double, and triple quotes, covering multi-line literals, escaping, and the absence of a distinct char type.

Learn how to access characters in Python strings using positive and negative indexes, and understand index out of range errors in string indexing from left to right.

Master slicing strings in Python by using the slice operator with begin and end indices, default values, and negative indices to extract substrings.

Explore the str data type and slice operator applications in Python, showing how to index strings, extract substrings, and convert selected characters to uppercase using positive and negative indices.

Explore how Python 3 handles string data with the + operator for concatenation and the * operator for repetition, including type rules and practical examples.

Explore type casting in Python, using int(), float(), complex(), bool(), and related rules to convert values between types, including when conversion is possible, base specification for strings, and common pitfalls.

Discover how to use python's float() and complex() for type casting, including one- and two-argument complex forms, constraints on converting complex to float, and boolean and string conversions.

Explore type casting in Python 3, focusing on bool() and str() conversions. Learn how numbers, strings, and complex values convert to boolean or string, with truthiness rules.

Explore Python typecasting with int, float, complex, str, and bool. Learn how single- or two-argument conversions work, including base handling and the limits of complex to float or int.

Explains immutability in Python, defines mutable vs immutable data types, and shows that assigning a new value creates a new object rather than changing the old one.

Explain immutability in Python by showing how objects are reused to save memory and boost performance, and why attempting to change content creates new objects to avoid affecting many references.

Understand immutability and mutability in Python's fundamental types. Observe how immutable objects like numbers stay constant, while lists are mutable and modify in place, with complex numbers treated specially.

Explore the Python list data type, mastering indexing and slicing, mutability, and operations such as append and remove, with duplicates and heterogeneous objects allowed.

Explore the tuple data type in Python: understand its immutability, how indexing and slicing work, how to access elements, and why single-element tuples require a trailing comma.

Explore Python's set data type, which disallows duplicates and is unordered. Learn to create an empty set with set() and note that indexing and slicing do not apply.

Examine the difference between sets and frozensets, highlighting immutable frozensets, how to create them, why duplicates are not allowed, and how mutability affects operations like add or remove.

Explore how Python dictionaries store key-value pairs with unique keys and potentially duplicate values. Discover mutability, basic usage, and access patterns for building and manipulating dicts.

Explore how Python's range creates numeric sequences using one, two, or three arguments, with immutable range objects you can loop, index, or slice.

Discover Python bytes and bytearray, compare immutable bytes with mutable bytearrays, and handle binary data such as images, videos, and audio within the 0–255 range.

Explore Python's fundamental data types—integers, floats, complex numbers, booleans, and strings—highlighting their immutability, indexing and slicing, and compare them with mutable collections like lists, sets, and dictionaries.

None represents no value in Python and is an object. Multiple variables can point to the same None object, while functions without a return value yield None.

Explore Python escape sequences and quotes, including backslashes, newlines, and tabs, with single- and multi-line comments; learn that constants are not enforced in Python, using uppercase conventions instead.

Explore how to read user input in Python, compare raw_input() and input(), and learn that Python 3's input returns a string, requiring explicit type casting.

Learn how to read two numbers from the keyboard with input, cast to integers, and print their sum, using single-line input and multiple approaches.

Read employee data from the keyboard and convert inputs to the correct types (typecasting), including employee number, name, salary, address, and marital status. Confirm the entered information before printing.

Read multiple values from keyboard in a single line with one input function, using split on spaces or commas and type casting to numbers, then unpack and compute the sum.

Explore how the eval() function automatically converts user input to the appropriate type and evaluates expressions to produce the resulting value.

Explore how to read command line arguments in Python. Pass them from the command prompt and access them as a list for printing and iteration.

Explore how command line arguments feed a Python program, demonstrate summing numeric inputs, handle string vs int types, cast inputs to integers, and manage no-argument cases.

Learn how Python command line arguments are split by spaces, how quotes group arguments, and how indexing the argument list reveals outputs or index errors; apply type casting for numbers.

Learn to use python's print() and its sep attribute to display output, handle multiple arguments, apply escape characters, and perform basic string operations.

Discover how Python print statements use end and sep attributes to control line breaks and separators. Learn the default newline behavior and how to customize output across multiple arguments.

Explore printing any Python object and its string representation, then master the replacement operator for format strings, using positional and keyword arguments with practical examples.

Learn how to print with formatted strings in Python using placeholders and variables, compare with the replacement operator, and apply width, precision, and type formatting.

Identify two error types in Python: syntax errors and runtime exceptions. Learn how exception handling targets runtime issues using try, except, else, and finally.

The lecture clarifies what an exception is and why exception handling aims for graceful termination rather than abnormal termination, by finding alternate events to continue when errors occur.

Understand default exception handling in Python and the exception hierarchy, from base exceptions to specific classes like keyboard interrupt and index errors, with graceful termination via handling.

Explore customized exception handling in python 3, focusing on graceful termination and avoiding resource leaks. Learn how try-except blocks manage risky code and ensure normal termination.

Explain how try-except controls program flow with matching except blocks and finally, covering what happens on normal or abnormal termination.

Learn how to print exception information to the console in Python 3 by accessing the exception object in an except block, printing its type, class name, and descriptive message.

Understand how to use multiple except blocks in Python, matching exception types with top-to-bottom order and handling common cases such as file not found or division errors.

Learn to use a single except block to handle multiple exceptions by listing them in a single block, replacing separate blocks with shared handling.

Explain the default except block and how it fires when no other block matches, plus how single and multiple except blocks (with optional parentheses) are ordered in Python.

Learn how the finally block ensures cleanup of resources, such as database connections, by always executing after try and except, providing reliable cleanup regardless of exceptions.

The lecture clarifies that the finally block in Python runs unconditionally after try and except, performing cleanup, but does not run if the program exits via os._exit.

Learn the difference between the finally block and destructor in Python, and how both manage cleanup and resource release before object destruction.

Explore the control flow of try, except, and finally in Python, showing how finally runs regardless of termination and how matched or unmatched exceptions behave.

Explore nested try-except-finally blocks and their control flow, with a demo program illustrating how finally and corresponding except blocks execute or skip based on exceptions.

Explore control flow in nested try-except-finally blocks in Python 3, detailing how 14 cases of exceptions interact with corresponding except clauses and finally executions.

Explore the else block in try-except-finally, showing how control flows when no exception occurs, and how finally performs cleanup in Python.

Learn how the else, except, and finally blocks interact in Python 3 through demo programs, illustrating when finally runs, how else executes with no exceptions, and file cleanup.

Explore the valid and invalid combinations of try-except-else-finally in Python, mastering syntax rules, multiple excepts, and how finally blocks interact.

Explore the two types of Python exceptions: predefined inbuilt exceptions and user-defined, programmatic exceptions raised by developers.

Define customized exceptions as classes derived from the base exception, implement a constructor to store a message, and raise and handle them with domain-specific names such as too young or too old.