Solve problems that are commonly asked in Python coding interviews related to common data structures.

Ace the Python Coding Interview

Data structures are among the fundamentals of computer science and are often a core decision in developing efficient programs. They are categorized as a vital benchmark of computer science knowledge when it comes to industry interviews. This module contains a detailed review of common data structures in Python. We'll also become well-equipped with different data structures by implementing them.
By the end of this module, we'll have expertise in solving problems in data structures.

Data Structures

# Lists vs Arrays

## Differences
Python lists are very flexible and can hold completely heterogeneous arbitrary data, but they use a lot more space than Python arrays. Each list contains pointers to a block of pointers, each of which in turn points to a full Python object. Again, the advantage of the list is flexibility: because each list element is a full structure containing both data and type information, the list can be filled with data of any desired type. Arrays lack this flexibility but are much more efficient for storing and manipulating data.


The differences between the two largely exist because of the aforementioned backend implementation. Arrays in Python are implemented just like C arrays, with a pointer pointing to the first element of the array with the rest existing contiguously in the memory.

 

Lists vs Arrays in Python

Lists vs Arrays

Differences