💡 Problem Formulation: This article addresses the task of generating a boolean array where each element represents whether a string in an input array ends with a specified suffix. For example, given the array [“hello.py”, “notes.txt”, “script.py”] and the suffix “.py”, the desired output is [True, False, True], since only “hello.py” and “script.py” end with “.py”.
Method 1: Using List Comprehension and str.endswith()
This method involves iterating over the input array using list comprehension and utilizing the str.endswith() method, which checks if a string ends with the specified suffix. It is a concise and pythonic way to solve the problem.
Here’s an example:
strings = ["hello.py", "notes.txt", "script.py"] suffix = ".py" boolean_array = [s.endswith(suffix) for s in strings]
Output: [True, False, True]
The code snippet above creates a boolean array indicating whether each string in the ‘strings’ list ends with the suffix “.py”. The str.endswith() method is called for each string in the array to perform this check.
Method 2: Using a Function and map()
map() is a built-in Python function that applies a specified function to each item of an iterable. In this case, we can create a function that leverages str.endswith() and map it across our array of strings, resulting in a clean and functional approach.
Here’s an example:
def ends_with_suffix(s, suffix):
return s.endswith(suffix)
strings = ["hello.py", "notes.txt", "script.py"]
suffix = ".py"
boolean_array = list(map(lambda s: ends_with_suffix(s, suffix), strings))Output: [True, False, True]
The function ends_with_suffix() is defined to return the result of str.endswith(). The map function then applies this across ‘strings’, and the result is converted back to a list to obtain a boolean array.
Method 3: Using Regular Expressions
Regular expressions can be used when more complex pattern matching is required. By compiling a regex pattern that matches the given suffix at the end of a string, we can iterate over the array and apply this pattern using the re.search() method. This method is powerful but can be overkill if the suffix is simple.
Here’s an example:
import re
strings = ["hello.py", "notes.txt", "script.py"]
suffix = ".py"
suffix_pattern = re.compile(rf"{suffix}$")
boolean_array = [bool(suffix_pattern.search(s)) for s in strings]Output: [True, False, True]
The regular expression defined by suffix_pattern looks for the suffix at the end of the string (denoted by the dollar symbol). The bool() function converts the match object to a boolean.
Method 4: Using filter() and lambda
This method leverages the filter() function, which constructs an iterator from those elements of iterable for which a function returns true. Combined with a lambda function to encapsulate our test, this provides a functional programming approach.
Here’s an example:
strings = ["hello.py", "notes.txt", "script.py"] suffix = ".py" boolean_array = list(map(lambda s: s.endswith(suffix), strings))
Output: [True, False, True]
The filter() function is not used here directly, as map() paired with a lambda provides a more direct solution to create a boolean array. The boolean results are then transformed into a list.
Bonus One-Liner Method 5: Generator Expression
Generator expressions offer a memory-efficient way to handle large datasets. This one-liner uses a generator within the list() constructor to simultaneously apply the suffix check and construct the boolean array without holding the entire array in memory.
Here’s an example:
strings = ["hello.py", "notes.txt", "script.py"] suffix = ".py" boolean_array = list(s.endswith(suffix) for s in strings)
Output: [True, False, True]
The example showcases a compact one-liner that constructs a boolean array. The expression s.endswith(suffix) is evaluated lazily for each string in ‘strings’ as the list constructor iterates over it.
Summary/Discussion
- Method 1: List Comprehension with
str.endswith(). Strengths: Straightforward and readable. Weaknesses: Not as memory-efficient for large datasets. - Method 2: Using
map()Function. Strengths: Functional programming style, extendable for complex logic. Weaknesses: Slightly less readable for those unfamiliar with functional concepts. - Method 3: Regular Expressions. Strengths: Highly powerful for pattern matching. Weaknesses: Overcomplicated for simple suffix matching, can be less performant due to regex overhead.
- Method 4: Using
filter()and lambda. Strengths: Functional programming style, efficient for filtering operations. Weaknesses: Incorrect use in example, should bemap()notfilter(). - Bonus Method 5: Generator Expression. Strengths: Memory-efficient for very large datasets. Weaknesses: Can be less intuitive to read and understand.