Python Lists and Arrays

Lists are ordered, mutable, and can contain elements of different types.

Lists are used by many algorithms.

[]

# Empty list x = [] # List with initial values y = [1, 2, 3, 4, 5] # List with mixed types z = [1, "hello", 3.14, True] Try it Yourself »

Python lists come with several built-in algorithms (called methods), to perform common operations like appending, sorting, and more.

Append one element to the list, and sort the list ascending: x = [9, 12, 7, 4, 11] # Add element: x.append(8) # Sort list ascending: x.sort() Try it Yourself »

Sometimes we want to perform actions that are not built into Python. Then we can create our own algorithms. For example, an algorithm can be used to find the lowest value in a list, like in the example below:

Create an algorithm to find the lowest value in a list: my_array = [7, 12, 9, 4, 11, 8]

Try it Yourself » The algorithm above is very simple, and fast enough for small data sets, but if the data is big enough, any algorithm will take time to run. This is where optimization comes in. Optimization is an important part of algorithm development, and of course, an important part of DSA programming.

When exploring algorithms, we often look at how much time an algorithm takes to run relative to the size of the data set. In the example above, the time the algorithm needs to run is proportional, or linear, to the size of the data set. This is because the algorithm must visit every array element one time to find the lowest value. The loop must run 5 times since there are 5 values in the array. And if the array had 1000 values, the loop would have to run 1000 times. Try the simulation below to see this relationship between the number of compare operations needed to find the lowest value, and the size of the array. See this page for a more thorough explanation of what time complexity is. Each algorithm in this tutorial will be presented together with its time complexity.