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Python Concepts for Data Science: List Comprehension

List comprehension is an elegant way to define and create lists based on existing lists. It is generally more compact and faster than normal functions and loops for creating lists.

The syntax:

newlist = [expression for item in iterable if condition == True]

The return value is a new list, leaving the old list unchanged. Here, the 'condition' is like a filter that only accepts the items that evaluate to True. The 'iterable' can be any iterable object, like a list, tuple, set etc. The 'expression' is the current item in the iteration, but it is also the outcome, which you can manipulate before it ends up like a list item in the new list. Example: Let us take an example of creation of a new list based on both with and without list comprehension. Suppose, based on a list of fruits, we want a new list, containing only the fruits with the letter "a" in the name. Without list comprehension:

fruits = ["apple", "banana", "cherry", "kiwi", "mango"]
newlist = []


for x in fruits:
  if "a" in x:
    newlist.append(x)


print(newlist)

Output:

['apple', 'banana', 'mango']


With list comprehension:

fruits = ["apple", "banana", "cherry", "kiwi", "mango"]


newlist = [x for x in fruits if "a" in x]


print(newlist)

Output:


['apple', 'banana', 'mango']



Conditionals in List Comprehension

1. Using if with List Comprehension

Example:

number_list = [ x for x in range(20) if x % 2 == 0]
print(number_list)

Output:

[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]


2. Nested IF with List Comprehension

Example:

num_list = [y for y in range(100) if y % 2 == 0 if y % 5 == 0]
print(num_list)

Output:

[0, 10, 20, 30, 40, 50, 60, 70, 80, 90]


3. if...else With List Comprehension

Example:

obj = ["Even" if i%2==0 else "Odd" for i in range(10)]
print(obj)

Output:

['Even', 'Odd', 'Even', 'Odd', 'Even', 'Odd', 'Even', 'Odd', 'Even', 'Odd']


Nested Loops in List Comprehension

For this, we will use the transpose of a matrix using nested loops.

matrix = [[1, 2], [3,4], [5,6], [7,8]]
transpose = [[row[i] for row in matrix] for i in range(2)]
print(transpose)

Output:

[[1, 3, 5, 7], [2, 4, 6, 8]]

One main benefit of using a list comprehension in Python is that it’s a single tool that we can use in many different situations. However, they’re not the right choice for all circumstances as they might make the code run more slowly or use more memory. Hence, based on the requirements, one can choose list comprehension or other alternatives.

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