How do I slice lists in Python for production systems?

In Python, slicing lists is a powerful feature that allows you to access a portion of a list without modifying the original list. This technique is particularly useful in production systems for data manipulation and retrieval.

To slice a list, you can use the following syntax:

my_list[start:end:step]

Here, start is the index to begin the slice (inclusive), end is the index to end the slice (exclusive), and step is the interval between items in the slice.

For example, consider the following list:

my_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] slice1 = my_list[2:5] # Returns [2, 3, 4] slice2 = my_list[:3] # Returns [0, 1, 2] slice3 = my_list[5:] # Returns [5, 6, 7, 8, 9] slice4 = my_list[::2] # Returns [0, 2, 4, 6, 8] slice5 = my_list[::-1] # Returns [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

Python slicing lists production systems data manipulation

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?