💡 Problem Formulation: The task is to create a boolean array in Python, indicating whether each element in a given array of strings ends with a specified suffix. For example, given an array ['Python', 'Cython', 'Pyth', 'typhon'] and a suffix 'on', the output should be a boolean array [True, True, False, True].
Method 1: Using List Comprehension
This method takes advantage of Python’s list comprehension feature to build a boolean array. The expression str.endswith(suffix) is used within a list comprehension to iterate over each string in the input list and check if it ends with the given suffix, resulting in the desired boolean array.
Here’s an example:
strings = ['Python', 'Cython', 'Pyth', 'typhon'] suffix = 'on' bool_array = [s.endswith(suffix) for s in strings] print(bool_array)
Output:
[True, True, False, True]
The given code snippet iterates over each element of the array strings, applies the str.endswith(suffix) method, and collects the results into a new list called bool_array. This is a concise and readable way to solve the problem.
Method 2: Using the Map Function
The map function in Python applies a given function to every item of an iterable and returns a list of the results. Here, we use a lambda function that checks if a string ends with the specified suffix.
Here’s an example:
strings = ['hello', 'world', 'python', 'code'] suffix = 'on' bool_array = list(map(lambda s: s.endswith(suffix), strings)) print(bool_array)
Output:
[False, False, True, False]
In this snippet, the lambda function lambda s: s.endswith(suffix) is iterated over each element courtesy of the map function, leading to a map object that, when converted to a list, forms the boolean array.
Method 3: Using a For Loop
For those who prefer traditional for loops, this method iteratively checks each string and appends the result to a new boolean array. This is the most explicit method and may be easiest for beginners to understand.
Here’s an example:
strings = ['check', 'suffix', 'boolean', 'array']
suffix = 'ay'
bool_array = []
for s in strings:
bool_array.append(s.endswith(suffix))
print(bool_array)Output:
[False, False, False, True]
The code snippet explicitly constructs the bool_array by using a for loop to append the result of the endswith method for each string. While not as concise as list comprehension, it’s clear and straightforward.
Method 4: Using Regular Expressions
Regular expressions can also be used to solve this problem. The re module in Python can check if strings end with a certain pattern. This method is powerful and flexible, suitable for complex pattern matching.
Here’s an example:
import re strings = ['end', 'trend', 'bend', 'send'] suffix = 'end' pattern = re.compile(suffix + r'$') bool_array = [bool(pattern.search(s)) for s in strings] print(bool_array)
Output:
[True, True, True, True]
The code snippet compiles a regular expression pattern that matches the suffix at the end of the string, and the list comprehension uses this pattern to create the boolean array. This method has the advantage of being adaptable to more complex suffix conditions.
Bonus One-Liner Method 5: Using NumPy Vectorization
For those who work with large datasets, NumPy’s vectorization capabilities can offer a significant performance boost. The numpy.char.endswith() function applies the endswith operation over an array of strings in an element-wise fashion.
Here’s an example:
import numpy as np strings = np.array(['rain', 'train', 'plane', 'game']) suffix = 'in' bool_array = np.char.endswith(strings, suffix) print(bool_array)
Output:
[True, True, False, False]
This one-liner leverages NumPy’s char.endswith method to process the array of strings and directly return the resulting boolean array. It is a highly efficient method when working with numerical computations and large data.
Summary/Discussion
- Method 1: List Comprehension. Strengths: Concise and pythonic. Weaknesses: May be slightly less explicit for those new to Python.
- Method 2: Map Function. Strengths: Functional programming approach, good for one-liners. Weaknesses: Requires conversion to list and may be less readable.
- Method 3: For Loop. Strengths: Explicit, straightforward, good for beginners. Weaknesses: More verbose and may be slower for large datasets.
- Method 4: Regular Expressions. Strengths: Very powerful and flexible for complex patterns. Weaknesses: Can be overkill for simple tasks and can be less readable.
- Method 5: NumPy Vectorization. Strengths: Highly efficient for large datasets. Weaknesses: Requires NumPy, not suitable for minimal or isolated tasks.