Python | Find all close matches of input string from a list

We are given a list of pattern strings and a single input string. We need to find all possible close good enough matches of input string into list of pattern strings. Examples:

Input : patterns = ['ape', 'apple', 
                  'peach', 'puppy'], 
          input = 'appel'
Output : ['apple', 'ape']

We can solve this problem in python quickly using in built function difflib.get_close_matches().

How does difflib.get_close_matches() function work in Python ?

difflib.get_close_matches(word, possibilities, n, cutoff) accepts four parameters in which n, cutoff are optional. word is a sequence for which close matches are desired, possibilities is a list of sequences against which to match word. Optional argument n (default 3) is the maximum number of close matches to return, n must be greater than 0. Optional argument cutoff (default 0.6) is a float in the range [0, 1]. Possibilities that don’t score at least that similar to word are ignored. The best (no more than n) matches among the possibilities are returned in a list, sorted by similarity score, most similar first.

Python3

# Function to find all close matches of
# input string in given list of possible strings
from difflib import get_close_matches

def closeMatches(patterns, word):
     print(get_close_matches(word, patterns))

# Driver program
if __name__ == "__main__":
    word = 'appel'
    patterns = ['ape', 'apple', 'peach', 'puppy']
    closeMatches(patterns, word)

References : https://docs.python.org/2/library/difflib.html

Output:

['apple', 'ape']

Time complexity : O(n*m), where n is the number of elements in the input list patterns, and m is the length of the input string word. The reason for this is that the get_close_matches() function from the difflib library uses a dynamic programming algorithm to find approximate matches, which can take O(nm) time. 

Space complexity :  O(n), as it only uses a fixed amount of memory, regardless of the size of the input string.