What are Dict and List comprehensions?

1.	What are Dict and List comprehensions?
Answer» Python comprehensions, like DECORATORS, are syntactic sugar constructs that help build altered and filtered lists, dictionaries, or SETS from a given list, dictionary, or set. Using comprehensions saves a lot of time and code that might be considerably more verbose (containing more LINES of code). Let's check out some examples, where comprehensions can be truly beneficial: PERFORMING mathematical operations on the entire list my_list = [2, 3, 5, 7, 11]squared_list = [x2 for x in my_list] # list comprehension# output => [4 , 9 , 25 , 49 , 121]squared_dict = {x:x2 for x in my_list} # dict comprehension# output => {11: 121, 2: 4 , 3: 9 , 5: 25 , 7: 49} Performing conditional filtering operations on the entire list my_list = [2, 3, 5, 7, 11]squared_list = [x2 for x in my_list if x%2 != 0] # list comprehension# output => [9 , 25 , 49 , 121]squared_dict = {x:x2 for x in my_list if x%2 != 0} # dict comprehension# output => {11: 121, 3: 9 , 5: 25 , 7: 49} Combining multiple lists into one Comprehensions allow for multiple iterators and hence, can be used to combine multiple lists into one. a = [1, 2, 3]b = [7, 8, 9][(x + y) for (x,y) in zip(a,b)] # parallel iterators# output => [8, 10, 12][(x,y) for x in a for y in b] # nested iterators# output => [(1, 7), (1, 8), (1, 9), (2, 7), (2, 8), (2, 9), (3, 7), (3, 8), (3, 9)] Flattening a multi-dimensional list A similar approach of nested iterators (as above) can be applied to flatten a multi-dimensional list or work upon its inner elements. my_list = [[10,20,30],[40,50,60],[70,80,90]]flattened = [x for temp in my_list for x in temp]# output => [10, 20, 30, 40, 50, 60, 70, 80, 90] Note: List comprehensions have the same effect as the map method in other languages. They follow the mathematical set builder notation rather than map and filter functions in Python.

Answer»

Python comprehensions, like DECORATORS, are syntactic sugar constructs that help build altered and filtered lists, dictionaries, or SETS from a given list, dictionary, or set. Using comprehensions saves a lot of time and code that might be considerably more verbose (containing more LINES of code). Let's check out some examples, where comprehensions can be truly beneficial:

PERFORMING mathematical operations on the entire list

my_list = [2, 3, 5, 7, 11]squared_list = [x**2 for x in my_list] # list comprehension# output => [4 , 9 , 25 , 49 , 121]squared_dict = {x:x**2 for x in my_list} # dict comprehension# output => {11: 121, 2: 4 , 3: 9 , 5: 25 , 7: 49}

Performing conditional filtering operations on the entire list

my_list = [2, 3, 5, 7, 11]squared_list = [x**2 for x in my_list if x%2 != 0] # list comprehension# output => [9 , 25 , 49 , 121]squared_dict = {x:x**2 for x in my_list if x%2 != 0} # dict comprehension# output => {11: 121, 3: 9 , 5: 25 , 7: 49}

Combining multiple lists into one

Comprehensions allow for multiple iterators and hence, can be used to combine multiple lists into one.

a = [1, 2, 3]b = [7, 8, 9][(x + y) for (x,y) in zip(a,b)] # parallel iterators# output => [8, 10, 12][(x,y) for x in a for y in b] # nested iterators# output => [(1, 7), (1, 8), (1, 9), (2, 7), (2, 8), (2, 9), (3, 7), (3, 8), (3, 9)]

Flattening a multi-dimensional list

A similar approach of nested iterators (as above) can be applied to flatten a multi-dimensional list or work upon its inner elements.

my_list = [[10,20,30],[40,50,60],[70,80,90]]flattened = [x for temp in my_list for x in temp]# output => [10, 20, 30, 40, 50, 60, 70, 80, 90]

Note: List comprehensions have the same effect as the map method in other languages. They follow the mathematical set builder notation rather than map and filter functions in Python.

What are Dict and List comprehensions?

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