Data Structures in Python: Learn, Apply, and Master

Learn how Python lists work as dynamic arrays that store an ordered collection of items, numbers, strings, or nested lists—and grow or shrink with add, remove, and access operations.

Explore Python list access with zero-based indexing and negative one for the last item, and learn slicing to extract sublists like 20 to 30.

Learn how Python lists are mutable and use append, insert, remove, pop, and clear to add, delete, or reorder items, including index-based insertions and value removals with duplicates.

Explore key list functions in Python, including length, max, min, and sum, with practical examples that print list size, find extrema, and total values.

Learn how to use lists in Python with append, print, pop, and sorting; compute average grades using sum and len, concatenate lists with plus, and note that lists are mutable.

Explore python tuples, which are immutable, using a four-element tuple named my_table. Access elements by index (0 first, -1 last, 2 for 30) and slice to obtain subtuples.

Learn how to concatenate tuples with the plus operator, repeat elements with multiplication, and test membership using the in keyword on immutable tuples.

Explore Python's built-in table functions on a four-element list, retrieving min, max, and count, then converting the list to a table and contrasting tuple and list.

Learn the difference between lists and tuples in Python. Use lists for dynamic collections, since they are mutable and slower, while tuples are immutable and faster for fixed data.

Explore Python dictionaries as key–value mappings, which are unordered yet highly efficient for lookup, showcasing mutability, updates, creation, access by key, and default values.

Master adding, updating, and removing dictionary entries using keys and values, with case sensitive keys; use del, pop, and clear, and test membership with in and not in.

Explore Python dictionary methods such as keys, values, and items, which return key-value pairs as tuples. Understand update: if a key exists, it updates; if not, it appends; clear resets.

Explore dictionary use cases in Python, from storing user info and product prices to counting characters, storing app configurations, phonebooks, e-commerce categorization, API responses, and inventory management.

Explore Python sets as unordered collections of unique, immutable elements that remove duplicates, defined with curly braces, support union, intersection, and difference, and enable membership testing instead of indexing.

This lecture demonstrates set operations in Python, including add, remove, discard, and clear, and explains handling missing elements and potential errors when removing non-existent items.

Explore Python set operations like union, difference, intersection, and symmetric difference, and see how sets enforce uniqueness by eliminating duplicates in examples with values 1 to 3.

Explore how sets remove duplicates from lists, find common elements, and track seen items, while extracting unique words and separating odd and even values with Python.

Practice data structures as the backbone of real-world applications by creating, accessing, modifying, and performing sorting and searching, while comparing sets, lists, and tables for efficiency and readability in Python.