Understanding time complexity is crucial for writing efficient code. This concept describes how an algorithm's runtime increases with the size of its input data. In Python, built-in functions and data structures have specific time complexities that can impact application performance. Knowing these complexities helps developers make informed decisions when choosing algorithms and data structures.

For instance, accessing an element in a list or dictionary is O(1), while inserting or removing elements is O(n). Similarly, sorting a list is O(n log n), whereas searching for a substring is O(n*m).

By understanding the time complexity of Python functions and data structures, developers can optimize their code for better performance.

Source: https://dev.to/williams-37/understanding-time-complexity-in-python-functions-5ehi