Advanced python: comprehension

In this article, we’ll learn about a Python language construct known as comprehensions for creating a new sequence based on existing one but its syntax is more human-readable than lambda functions. These can be applied to lists, sets, and dictionaries. 

REVIEW Lambda and map

Lambda is an anonymous function (function without name). Let have a quick look at the following example for its syntax:

def square(a):
    return a*a

We can rewrite the above function as:

Now, recall the map function over a list. It takes a function as a parameter and applies to each member of a list of values. The map() function executes a specified function for each item in an iterable. The item is sent to the function as a parameter. For example:

#create a list of lengths from a list of given names.
def myfunc(a):
  return len(a)
x = map(myfunc, ('apple', 'banana', 'cherry'))
print(x)
#convert the map into a list, for readability:
print(list(x))
#[5, 6, 6]

List Comprehension with filter

Firstly, let’s use the map to create a list of numbers that are squares of the original numbers in the list using lambda (small anonymous function).

# define two lists of numbers
evens = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
odds = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
# Perform a mapping square for evens
evenSquared = list(map(lambda e: e**2, evens))
print(evenSquared)
#[4, 16, 36, 64, 100, 144, 196, 256, 324, 400]

Now, we want to apply the square function to numbers that are greater than 4 and less than 16. We can modify the mapping function as follow.

evenSquared = list(map(lambda e: e**2, filter(lambda e: e > 4 and e < 16, evens)))
#[36, 64, 100, 144, 196]

List comprehensions are usually more human-readable than lambda functions. It is easier to understand what the programmer was trying to accomplish when using list comprehensions.

# Limit the items operated on with a predicate condition
oddSquared = [e ** 2 for e in odds if e > 3 and e < 17]
print(oddSquared)
#[36, 64, 100, 144, 196]

Set comprehension

In the following example, we converted temperatures from one scale to another using this kind of approach.

# define a list of temperature data points
ctemps = [5, 10, 12, 14, 10, 23, 41, 30, 12, 24, 12, 18, 29]
# build a set of unique Fahrenheit temperatures
ftemps1 = [(t * 9/5) + 32 for t in ctemps]
ftemps2 = {(t * 9/5) + 32 for t in ctemps}
print(ftemps1)
#[41.0, 50.0, 53.6, 57.2, 50.0, 73.4, 105.8, 86.0, 53.6, 75.2, 53.6, 64.4, 84.2]
print(ftemps2)
#{64.4, 73.4, 41.0, 105.8, 75.2, 50.0, 84.2, 53.6, 86.0, 57.2}

We can also apply to characters in a String.

# build a set from an input source
sTemp = "The quick brown fox jumped over the lazy dog"
chars = {c.upper() for c in sTemp if not c.isspace()}
print(chars)
#{'B', 'U', 'P', 'V', 'Q', ..., 'F', 'R', 'O', 'X', 'L', 'C'}

Dictionary comprehensions

With the same technique, handling dictionary with comprehension is rather easy and convenient. We can populate a new dictionary with inline key and value arguments.

In the following example, we have two teams of members (keys) and their scores (values). Now, we will create a new dictionary by merging the two teams using comprehension.

# Merge two dictionaries with a comprehension
team1 = {"Jones": 24, "Jameson": 18, "Smith": 58, "Burns": 7}
team2 = {"White": 12, "Macke": 88, "Perce": 4}
newTeam = {k: v for team in (team1, team2) for k, v in team.items()}
print(newTeam)
#{'Jones': 24, 'Jameson': 18, 'Smith': 58, 'Burns': 7, 'White': 12, 'Macke': 88, 'Perce': 4}

You can read more about advanced python from these articles.