Map & Filter

In this note, we will be covering some useful built-in functions to help us extend our list comprehension capabilities.

These two functions that we learn will also be revisited when we do functional programming and lambda functions.

The Map Function

The idea of a map function is to apply a function to an iterable data.

Formatting:

map(function_name, sequence)

-- function_name: any function (built-in or selfmade) that returns a desired value of choice
-- sequence: any iterable data type

# Example
def square(num):
    ''' squares the given num argument '''
    return num ** 2
# end of square

array = list(range(1,11))
square_array = list(map(square, array))

print('Original Array:', array)
print('Array Squared:', square_array)

Original Array: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Array Squared: [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

One thing to note about the map function is that it doesn’t return a specific data type, but rather, an python iterable data. Therefore, after we apply the map function, we just execute a list function on it.

# Example 2

def upper(x):
    ''' upper() turns string x into its uppercased counter part '''
    return x.upper()

word = 'hello world!'
upper_word = ''.join(list(map(upper, word)))

print(word)
print(upper_word)

# simpler way:
print(word.upper())

hello world!
HELLO WORLD!
HELLO WORLD!

In example 2, we are doing a lot of unnecessary work to make our original word variable uppercased. This is an example of how you shouldn’t use map() for every little changes you want to a string.

This also applies to all data structure that has methods. You don’t want to use methods with map, since there is a high probablity that there is already method for what you might want to do.

Filter Function

The idea of the filter function is to filter out items from a data set that meets a certain condition.

Formatting:

filter(bool_returning_function, sequence)

-- function: The function name we provide for filter() must be return a boolean value ... should also be able handle the items inside the sequence as its arguments
-- sequence: any iterable data type

# Example 3

def isOdd(x):
    ''' isOdd() returns True if x is odd.'''
    return x % 2 != 0

array = list(range(1,101))
odds = list(filter(isOdd, array))

print('Odd Numbers from 1 to 100:', odds)

Odd Numbers from 1 to 100: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99]

It is true that this can be done differently, but this was a simplistic use of filter to show we can filter out variables that satisfies condition… aka the condition is True.

Example Problem: List of Palindromic Numbers

Our goal in this example program is to create a list of palindromic numbers (numbers that are palindromes) from 1 to 10,000.

# Palindromic Numbers from 1 to 10000

def isPalindrome(x):
    ''' isPalindrome returns True if string X is a palindrome.'''
    return x == x[::-1]

array = list(range(1,10000))

palindromic_numbers = list(map(int, filter(isPalindrome, map(str, array))))
print('Palindromic Numbers from 1 to 10,000', palindromicNumbers)

Palindromic Numbers from 1 to 10,000 [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 22, 33, 44, 55, 66, 77, 88, 99, 101, 111, 121, 131, 141, 151, 161, 171, 181, 191, 202, 212, 222, 232, 242, 252, 262, 272, 282, 292, 303, 313, 323, 333, 343, 353, 363, 373, 383, 393, 404, 414, 424, 434, 444, 454, 464, 474, 484, 494, 505, 515, 525, 535, 545, 555, 565, 575, 585, 595, 606, 616, 626, 636, 646, 656, 666, 676, 686, 696, 707, 717, 727, 737, 747, 757, 767, 777, 787, 797, 808, 818, 828, 838, 848, 858, 868, 878, 888, 898, 909, 919, 929, 939, 949, 959, 969, 979, 989, 999, 1001, 1111, 1221, 1331, 1441, 1551, 1661, 1771, 1881, 1991, 2002, 2112, 2222, 2332, 2442, 2552, 2662, 2772, 2882, 2992, 3003, 3113, 3223, 3333, 3443, 3553, 3663, 3773, 3883, 3993, 4004, 4114, 4224, 4334, 4444, 4554, 4664, 4774, 4884, 4994, 5005, 5115, 5225, 5335, 5445, 5555, 5665, 5775, 5885, 5995, 6006, 6116, 6226, 6336, 6446, 6556, 6666, 6776, 6886, 6996, 7007, 7117, 7227, 7337, 7447, 7557, 7667, 7777, 7887, 7997, 8008, 8118, 8228, 8338, 8448, 8558, 8668, 8778, 8888, 8998, 9009, 9119, 9229, 9339, 9449, 9559, 9669, 9779, 9889, 9999]

Explanation

Function Composition Breakdown
1. string version of the array --> map(str, array)
2. filter out the palindrome --> filter(isPalindrome, string version of the array)
3. remap all values back to integers --> map(int, palindromes)
4. turn the mapped integers iterable back inside a list --> list(palindromicIterables)

How it would looked with multiple defined variables:

array = list(range(1,10000))
str_array = map(str, array)
palindromes = filter(isPalindrome, str_array)
palindromics = map(int, palindromes)

palindromic_numbers = list(palindromes)

Composition of Functions

Composition of Functions is the idea of using functions within a function call or apply one function to the result of another function. You can read more about them here.