Big O

This is the nicest intro and should clear things up.

Big-O Complexity	Name / Growth Rate	Typical Examples
$O(1)$	Constant	Array indexing, hash table lookup, pushing/popping from a stack or queue
$O(log n)$	Logarithmic	Binary search, tree search in balanced BST/AVL/Red-Black Tree, heap operations (insert/extract-min)
$O(n)$	Linear	Linear search, traversing an array or linked list, finding max/min in unsorted list
$O(n log n)$	Log-linear	Merge sort, heap sort, quicksort (average case), efficient sorting in general
$O(n²)$	Quadratic	Bubble sort, insertion sort, selection sort, checking all pairs in a list
$O(n³)$	Cubic	Naïve matrix multiplication, checking all triplets
$O(2^n)$	Exponential	Recursive solution to Traveling Salesman Problem (TSP), generating all subsets, naïve Fibonacci recursion
$O(n!)$	Factorial	Solving Traveling Salesman by brute force, generating all permutations

Operations on most Python data structures (e.g. appending a list, checking membership in a tuple) are $O(1)$, $O(log\space n)$, or $O(n)$.

Also note that if you do $O(n)$ things $m$ times, your complexity is $O(m \times n)$