5 Best Ways to Return a Boolean Array for String Prefix Match in Python

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πŸ’‘ Problem Formulation: The challenge is to generate a boolean array indicating which of the elements in an array of strings start with a specified prefix. For example, given an array ["apple", "banana", "apricot", "cherry"] and a prefix "ap", the desired output is a boolean array: [True, False, True, False], representing which strings begin with the prefix “ap”.

Method 1: Using a List Comprehension with str.startswith()

The str.startswith() method offers a straightforward way to test if a string begins with a specified prefix. Coupled with list comprehension, it provides an elegantly concise approach to construct the desired boolean array.

Here’s an example:

prefix = "ap"
fruits = ["apple", "banana", "apricot", "cherry"]
boolean_array = [fruit.startswith(prefix) for fruit in fruits]
print(boolean_array)

Output: [True, False, True, False]

This code snippet iterates over the list fruits and for each element, it checks whether the string starts with the prefix "ap". The result is a list of boolean values corresponding to each check.

Method 2: Using the map() Function with a Lambda Expression

The map() function applies a given function to every item of an iterable. When combined with a lambda expression that leverages str.startswith(), it becomes a powerful tool for creating the desired boolean array.

Here’s an example:

prefix = "ap"
fruits = ["apple", "banana", "apricot", "cherry"]
boolean_array = list(map(lambda fruit: fruit.startswith(prefix), fruits))
print(boolean_array)

Output: [True, False, True, False]

The map() function is used here with a lambda function that checks if each element in fruits starts with the prefix. The result is then converted to a list to obtain the boolean array.

Method 3: Using a For Loop

While not as succinct as list comprehensions, a for loop provides transparency in the generation of the boolean array, which might be preferred in certain scenarios for readability or debugging purposes.

Here’s an example:

prefix = "ap"
fruits = ["apple", "banana", "apricot", "cherry"]
boolean_array = []
for fruit in fruits:
    boolean_array.append(fruit.startswith(prefix))
print(boolean_array)

Output: [True, False, True, False]

A for loop processes each string in the fruits list, appends the result of startswith() to boolean_array, and finally prints the resulting array.

Method 4: Using Regular Expressions with List Comprehension

For scenarios where enhanced pattern matching is needed, Python’s re module can be utilized within a list comprehension to achieve the same result, offering greater flexibility in pattern matching.

Here’s an example:

import re
prefix = "ap"
fruits = ["apple", "banana", "apricot", "cherry"]
boolean_array = [bool(re.match(prefix, fruit)) for fruit in fruits]
print(boolean_array)

Output: [True, False, True, False]

Regular expressions via re.match() are used for each element in fruits. A boolean array is created by converting the match object to a boolean, yielding True for a match and False otherwise.

Bonus One-Liner Method 5: Using a List Comprehension and the filter() Function

A more functional approach can be employed using filter(). While not returning a boolean array directly, it can be utilized to achieve a similar result in a one-liner format, especially in the context of filtering iterables based on prefix presence.

Here’s an example:

prefix = "ap"
fruits = ["apple", "banana", "apricot", "cherry"]
boolean_array = [fruit in filter(lambda x: x.startswith(prefix), fruits) for fruit in fruits]
print(boolean_array)

Output: [True, False, True, False]

The example utilizes filter() which creates an iterator of elements that satisfy the condition, then the list comprehension checks if each element from the original list is in the filtered result, thereby creating the boolean array.

Summary/Discussion

Method 1: List Comprehension with str.startswith(). Strengths: Concise and readable. Weaknesses: Limited to simple prefix matching.
Method 2: map() Function with a Lambda Expression. Strengths: Functional programming style, concise. Weaknesses: May be less readable to those unfamiliar with functional concepts.
Method 3: Using a For Loop. Strengths: Clarity and explicit logic flow. Weaknesses: More verbose than other methods.
Method 4: Regular Expressions with List Comprehension. Strengths: Flexible and powerful pattern matching. Weaknesses: Can be overkill for simple tasks and less performant than string methods.
Method 5: List Comprehension and filter() Function. Strengths: Functional one-liner, elegant filtering. Weaknesses: Slightly less direct in returning a boolean array, can be less intuitive.