3 Advanced Operations in Python Lists with Examples | by Rukshan Pramoditha | Towards Data Science

The list is one of the built-in data types in Python. It is used to store multiple Python objects (integer, float, string, etc.) in a single variable. The elements are placed inside a pair of square brackets.
list_1 = [1, 0, 10] # A list of integerslist_2 = [5.3, 10.0, 7.5] # A list of floatslist_3 = ['A', 'I', 'Help', 'Thanks'] # A list of stringslist_4 = [1, 5.3, 'A'] # A list of mixed Python objects!
The individual elements in a list can be selected by using zero-based indexing (zero for the first element).
Lists are mutable. We can add or remove items or change the value of an element in a list after we create it.
Elements of different data types can be included in the same list.
The list() constructor can be used to create a new list.
A list of subsequent integers can be created by using the range() function inside the list() constructor.
There are many operations in Python lists. Here, more emphasis will be given to Merging, List Comprehension and Append Elements.
The zip() function merges two lists into a new list by taking corresponding elements from the two lists. The result is a zip object. We can place that object inside the list() constructor to reveal the new list of tuples.
list_1 = [1, 2, 3]list_2 = ['one', 'two', 'tree']list(zip(list_1, list_2))
We can also merge two lists with different lengths. In this case, the new list is created in favor of the shortest list.
list_1 = [1, 2, 3, 4, 5] # 5 elementslist_2 = ['one', 'two', 'tree'] # Only 3 elementslist(zip(list_1, list_2))
List comprehension is a one-line code technique used to build a new list by iterating the elements inside a list. The new list is created in the way that the original list elements satisfy a certain expression or condition or both.
The syntax is:
[<expression> for <variable> in <list>]
Imagine that we have the following list.
new_list = [1, 2, 3, 4, 5]
Now, we want to create a new list that contains the square values of all the elements in the above list. For this, we can use list comprehension.
[x**2 for x in new_list]
List comprehension can also contain a condition. In that case, the syntax is:
[<expression> for <variable> in <list> if <condition>]
Now, we want to create a new list that contains the square values of selected elements (e.g. odd numbers) in the original list. For this, we can use list comprehension with a condition.
[x**2 for x in new_list if x % 2 == 1]
The previous two operations did not modify the original lists on which the operations were done. But, append elements is an in-place operation that modifies the original list directly. This operation adds elements to the end of the list. This can be done by using the append() method of the list object.
Usually, we create an empty list and then append elements as we need. The above square-value problem can also be solved by using this approach.
a = [] # Empty listfor i in new_list:  sq = i**2  a.append(sq)a
At the creation, a is an empty list. Now, a has been modified and contains 5 elements meaning that the append operation was done in place.
The append operation is very useful when you want to store the calculated values for plotting at a later time. For example,
x = [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]y = []for i in x:  ex = i**3  y.append(ex)import matplotlib.pyplot as pltplt.plot(x, y)
The coordinates can be seen by using the merge operation!
coordinates = list(zip(x, y))coordinates
Python lists are very useful because we can perform some advanced operations with them using just one or two lines of code. A great alternative to Python lists is NumPy arrays, especially when we consider the performance. When executing, NumPy arrays are significantly faster than Python lists. However, we can easily create NumPy arrays using Python lists or convert lists into arrays. Python list is a built-in data type. It is closely associated with other data types and libraries in Python.
My readers can sign up for a membership through the following link to get full access to every story I write and I will receive a portion of your membership fee.
Sign-up link: https://rukshanpramoditha.medium.com/membership
Thank you so much for your continuous support! See you in the next story. Happy learning to everyone!
Special credit goes to Chris Lawton on Unsplash, who provides me with a nice cover image for this post.