Python assignment in list comprehension
List Comprehension in PythonPreviously in this Python tutorial, we have already learned what Python lists are and how to use them. We have also understood list comprehension in Python. In this section, we will go deep into this topic and understand why we need it. Show
What is Python List Comprehension?Some of the programming languages have a syntactic construct called list comprehension for creating lists on the basis of existing lists. Python is one such language. In other words, list comprehensions are used for converting one list into another list or creating a new list from other iterables.
Lets take a brief look over Python lists before starting with Python list comprehensions. Python ListsPython list is a compound data type in Python where we can group together various values. These values need not be of the same type; we can combine Boolean, string, and integer values together and save them as lists. Example: list1 = [1,2,3,4,5] list2=[hello,intellipaat]SyntaxThe syntax of List Comprehension Python consists of square brackets inside of which we write expressions followed by a for clause and then by for or if clauses as needed. [ expression for an item in list if conditional ] When compared to a normal Python Lists syntax, the above syntax is equivalent to for an item in the list: The resultant is a new list that is created after the evaluation of the expression in accordance with the for and if clauses provided after the expression. Following is the equivalent code in List Comprehension Python to obtain the same result: new_list = [expression(i) for i in old_list if filter(i)]Here,
Why Should We Use List Comprehension Python?List comprehension is an elegant and concise way of creating a list in Python. There are many benefits of using Python List and the most basic benefit can be seen from the syntax alone. We reduced three lines of code into a one-liner. Not just that but the code in Python List comprehension is much faster also. With Python List comprehension, instead of having to resize the list on runtime, python will now allocate the lists memory first, which makes it faster in this case. Now that we know the syntax of Python List comprehensions, let us try out some examples. Output: Example 2: Now lets create a list using the for clause in a list comprehension. cubes = [x**3 for x in range(5)] print (cubes)Output: [0, 1, 8, 27, 64]Example 3: Lets create a new list by making some changes in an already existing list. list1 = [3,4,5] new_multiplied_list = [item*2 for item in list1] print (new_multiplied_list)In the above code block, the new list is namednew_multiplied_list, and it is obtained by multiplying every element of the old list, which is named as list1, with 2. Example 4: Now lets create a new list containing the first letters of every element in an already existing list. listOfWords = ["this","is","python","tutorial","from","intellipaat"] new_list = [ word[0] for word in listOfWords ] print (new_list)Output: ['t', 'i', 'p', 't', 'f', 'i']List Comprehension Python vs For Loop in PythonWhenever we want to repeat a block of code for a fixed number of times, the first way of doing it is using for loops. List comprehensions are also capable of doing the same and that too with a better approach than for loops since list comprehensions are more compact. Hence, it provides a very good alternative for loops. The code block for the same in case of using for loop will be: letters = [] Output: ['I', 'n', 't', 'e', 'l', 'l', 'i', 'p', 'a', 'a', 't']We can obtain the same result using List Comprehension with lesser number of code lines as shown below: letters = [ letter for letter in 'Intellipaat' ] print(letters)Output: ['I', 'n', 't', 'e', 'l', 'l', 'i', 'p', 'a', 'a', 't']While using Python list comprehensions, we dont necessarily need a list to create another list. We can use strings as well, and list comprehension will identify it as a string and work on it as a list. In the above example of list comprehension code block,Intellipaatis a string, not a list. List Comprehension Python Vs. Python Lambda FunctionsIn Python, we also use Lambda functions to modify and manipulate lists. Lambda functions are also known as anonymous functions. Lambda functions are usually used with various built-in functions such as map() filter(), and reduce() to work on lists. Map() with Lambda FunctionLets first see how we use map() with Lambda function to work on lists: letters = list(map(lambda x: x, 'intellipaat')) print(letters)In the above block of code, we have used map() with lambda to create a list containing the letters of the string intellipaat separated by commas. The name of the list is the letters. We can obtain the same result using Python List Comprehensions. The codes in Python List comprehensions are also more human-readable and easier to understand.
So the final code using Python List Comprehension looks like this: New_list=[ x for x in 'intellipaat'] print(new_list)Output: ['i', 'n', 't', 'e', 'l', 'l', 'i', 'p', 'a', 'a', 't']Wish to gain an in-depth knowledge of python? Check out our Python Tutorial and gather more insights! Filter() with Lambda FunctionNow that we know how we can use Python list comprehension as an alternative for the map() function combined with lambda, lets now see how we can use Python list comprehensions as an alternative for the filter() function. We have used filter() with lambda to filter out odd values from an existing list and save the filtered values in a new list in the following code block. list1 = [1,2,3,4,5,6,7,8,9,10] list1 = list(map(int, list1)) new_list= filter(lambda x: x%2, list1) print(list(new_list))Output: [1, 3, 5, 7, 9]The same result can be obtained using Python List Comprehension as shown below: list1 = [1,2,3,4,5,6,7,8,9,10] print(list1) new_list = [ x for x in list1 if x%2 ] print(new_list)Output: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] [1, 3, 5, 7, 9]Reduce() with Lambda FunctionAs mentioned above, we can also write the lambda function codes with the reduce() function to make the code short and more efficient using Python list comprehensions. Note: Recently, the reduce module was moved to the functools package, so If we are using Python 3, then we will have to import the reduce module from functools as shown in the above code block. The same result can be obtained using Python list comprehensions as shown below: Output: 21Note: Here, we did not have to import the reduce module, because we replaced it with Python list comprehension. Conditionals in Python List ComprehensionWe can also make use of conditional statements in list comprehensions to modify and manipulate lists. Lets learn how to do that with the help of some examples. Here, we will use a mathematical function range() that defines the range that we want to use in our examples. It takes one integer as a parameter and the range starts from 0 to the number one less than the parameter provided. For example, range(20) will consider the range of numbers from 0 to Examples:1. Using if statement in Python List comprehension new_list = [x for x range(20) if x%2==0] print(new_list)Output: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]2. Using Nested IF with Python List Comprehension new_list = [x for x in range(50) if x %2==0 if x%5==0]Output: [0, 10, 20, 30, 40, 50]In the above example, the first condition that the list comprehension checks is if x is divisible by 2, and then if the condition is met it encounters the other conditional statement and checks if x which was found to be divisible by 2 is also divisible by 5. 3. Using if-else statement with Python List comprehension even_odd = [f"{x} is even" if x%2==0 else f"{x} is odd" for x in range (10)] print(even_odd)Output: ['0 is even', '1 is odd', '2 is even', '3 is odd', '4 is even', '5 is odd', '6 is even', '7 is odd', '8 is even', '9 is odd']In the above example, the list comprehension checks all numbers starting from 0 to 9. If x is found to be divisible by 2, x is even (where x is the respective number) is appended to the even_odd list. If the condition is not met, then the else statement is executed, and x is odd( where x is the respective number) is appended in the even_odd list. Nested Lists in Python List ComprehensionWhenever we talk about nested lists, the first method to implement nested lists that comes to our mind is using nested loops. As we have already seen that Python list comprehension can be used as an alternative for loops, obviously it can also be used for nested loops. Lets see how to do it with the help of an example. The output would be: 4*1=4 4*2=8 4*3=12 4*4=16 4*5=20 4*6=24 4*7=28 4*8=32 4*9=36 4*10=40 5*1=5 5*2=10 5*3=15 5*4=20 5*5=25 5*6=30 5*7=35 5*8=40 5*9=45 5*10=50 6*1=6 6*2=12 6*3=18 6*4=24 6*5=30 6*6=36 6*7=42 6*8=48 6*9=54 6*10=60We can obtain the same result using Python list comprehension, and the code block used will be: table = [[x*y for y in range(1,11)] for x in range(4,7)] Output: [[4, 8, 12, 16, 20, 24, 28, 32, 36, 40], [5, 10, 15, 20, 25, 30, 35, 40, 45, 50], [6, 12, 18, 24, 30, 36, 42, 48, 54, 60]]In the above example, we have used for loop for y as the inner comprehension. Key Takeaways
ConclusionPython list comprehensions are found to be faster and more user-friendly than for loops and lambda functions. The speed and readability are what makes Python list comprehensions a favourable alternative for loops and lambda function. Mastering this comprehension can make our codes more efficient, faster, and shorter. While we have covered all main topics, along with examples, there is more to learn about Python. Want to gain in-depth knowledge and up-to-date study material? Check out this well-structured Python Certification Training Courseby Intellipaat. This course will help master all main modules and packages in Python, web scraping libraries, lambda function, and more. Through this course, learn how to write Python codes for Big Data systems, such as Hadoop, and get plenty of hands-on exercises to practice and master Python. |