3 Python List Operations That Are Advanced That Can Help You Optimise Your Code

 

1.   List Comprehension

One advanced Python list operation that can effectively optimize your code is the use of list comprehension. List comprehension is a simple way to generate a list, compared to using loops. It saves time and reduces the amount of code needed to achieve the same result.

List comprehension is a concise way to create lists. It can be used in place of a for loop, and it creates a new list by applying an expression to each item in an existing list. The basic syntax of list comprehension is:

 

new_list = [expression for item in old_list]

 

Example:

 

>>> old_list = [1, 2, 3, 4, 5]

>>> new_list = [i * 2 for i in old_list]

>>> print(new_list)

[2, 4, 6, 8, 10]

 

In this example, the expression i * 2 is applied to each item in the old_list, and a new list is created containing the results.

2.   Vectorization

Another advanced Python list operation that can help optimize your code is vectorization. Vectorization is a technique that allows you to perform operations on entire arrays, without the need for explicit loops. This can greatly improve the performance of your code, as it avoids the overhead of looping.

Vectorization is particularly useful when working with large datasets or when performing operations that are computationally expensive. It can be used to perform mathematical operations, such as addition, subtraction, and multiplication, on entire arrays at once.

Example:

 

>>> import numpy as np

>>> a = np.array([1, 2, 3, 4, 5])

>>> b = np.array([6, 7, 8, 9, 10])

>>> c = a + b

>>> print(c)

[ 7 9 11 13 15]

 

In this example, the arrays a and b are added together using vectorization. The result is a new array c containing the sum of the elements in the original arrays.

3.   map() and filter()

Another advanced Python list operation that can help optimize your code is the use of the map() and filter() functions. These functions can be used to perform operations on lists in a concise and efficient way.

The map() function applies a given function to each item in a list, and returns a new list containing the results.

Example:

 

>>> old_list = [1, 2, 3, 4, 5]

>>> new_list = map(lambda x: x * 2, old_list)

>>> print(list(new_list))

[2, 4, 6, 8, 10]

 

The filter() function can be used to filter items from a list based on a given condition.