Learn Python Programming From A-Z: Beginner To Expert Course

Discover how Anaconda provides a one-stop Python data science toolkit, and explore Jupyter notebooks for interactive coding and documentation, as well as managing environments and libraries.

Learn how Python variables act as memory references, how to name them with lowercase underscores, and how to perform simple operations and use the print function.

Explore integers and floats in Python, learn how type inference and the type function reveal data types, and see how arithmetic yields float results.

Learn string manipulation in Python: concatenate with +, repeat by integers, and check substrings with in; use built-in functions like len, str, and int.

Index strings with square brackets using zero-based positions to access characters, including the first at index zero and the last with negative indices. Indices beyond the string length throw errors.

Explore string slicing in Python by using start:end indices and default stride of 1, learn zero- and negative-indexing, and apply count and find to locate substrings.

Master Python's logical operators not, or, and to evaluate truthiness and boolean expressions, understand how equality differs from truth value, and learn to use Stack Overflow for solutions.

Master Python membership operators by using in and not in to check if a sequence exists in another, with practical examples on strings and lists.

Explore bitwise operators in Python, including and, or, exclusive or, and bit shifts. Understand binary representation, how these operators process each bit to produce results.

Master sets in Python: store unique, unordered elements; learn to add and remove items with special functions, and why sets cannot be indexed.

Master while loops and their differences from for loops, including when to use them to avoid infinite loops, with sensor readings and accumulation examples.

Explore the continue statement role in skipping current iterations within for and while loops, and compare it with break to see practical uses like printing even numbers.

Explore the enumerate function, a zip-like tool that iterates over a single iterable while counting, returning index and value pairs; learn with a practical example.

Learn to define Python functions with def, specify inputs (including defaults), implement a function body with return, and name functions clearly while avoiding Python keywords and built-ins.

Learn how to document Python functions with doc strings using triple quotes, explaining inputs, outputs, and function behavior, and follow company standards and Python conventions such as BEP 257.

Explore error handling and user input in Python by distinguishing syntax errors from exceptions, and learn how to prompt and process user data in scripts.

Explore error handling fundamentals by distinguishing syntax errors from exceptions, see how a simple function can throw with strings, and practice catching and handling errors like division by zero.

Learn to capture user input with the input function, provide a helpful prompt, cast the input to an integer or float, and handle invalid input with a clear message.

Identify and fix syntax errors in Python by reading the interpreter's filename, line number, and error message, and learn how sequential fixes enable code to run.

Explore Python exceptions as runtime errors, including zero division, name, type, and assertion errors, and learn to handle them with try, except, and raise.

Learn to handle multiple exceptions in Python using try, except, else, and finally. Understand how zero division errors and assertion errors trigger different paths, and how finally affects return values.

Explore Python advanced functions, including lambda expressions, args and kwargs, iterators, generators with yield, and map and filter, with a focus on memory-efficient iteration.

Learn how Python iterators wrap a data stream, convert iterables to iterators, and traverse elements using next, with lazy evaluation for large datasets.

Explore the map function as a higher-order tool that applies a function to iterables, using lambda expressions, generators, and unpacking to write concise Python code.

Explore the filter function as a higher-order tool in Python, comparing it to map, returning booleans and generators, and converting results with list or lambdas.