💡 Problem Formulation: You have a list of strings in Python and you need to filter this list based on the presence or absence of a given substring. For example, from the input list ["apple", "banana", "cherry", "date", "apricot"], you want to extract only those strings that contain the substring "ap", resulting in the output ["apple", "apricot"]. This article will explore five strategies to accomplish this task.
Method 1: Using a List Comprehension with in
This method involves using a list comprehension with the in keyword to check for the presence of a substring in each element of the list. It is Pythonic, concise, and typically the first choice among Python developers for such tasks.
Here’s an example:
words = ["apple", "banana", "cherry", "date", "apricot"] substring = "ap" filtered_words = [word for word in words if substring in word] print(filtered_words)
Output:
['apple', 'apricot']
In the example, the list comprehension iterates over each word in the original list words and checks if the substring “ap” is part of the word. If it is, the word is included in the new list filtered_words.
Method 2: Using filter() with a Lambda Function
The filter() function in combination with a lambda function allows you to filter a list based on a condition. This method is more functional in style and works well with larger codebases following functional programming paradigms.
Here’s an example:
words = ["apple", "banana", "cherry", "date", "apricot"] substring = "ap" filtered_words = list(filter(lambda word: substring in word, words)) print(filtered_words)
Output:
['apple', 'apricot']
The filter() function applies a lambda function that checks whether the substring “ap” is in each word. The resulting iterator is then converted back to a list to obtain filtered_words.
Method 3: Using a Function and filter()
Alternatively to the lambda, you can define a stand-alone function and use it with filter(). This is particularly useful when the filtering logic is complex and needs to be reused in multiple places.
Here’s an example:
def contains_substring(substring, word):
return substring in word
words = ["apple", "banana", "cherry", "date", "apricot"]
substring = "ap"
filtered_words = list(filter(lambda word: contains_substring(substring, word), words))
print(filtered_words)
Output:
['apple', 'apricot']
The function contains_substring() is defined with the logic to check for the substring in a word. When iterating through the words list with filter(), the function is called for each word to determine if it should be included in the filtered_words list.
Method 4: Using Regular Expressions with re Module
When filtering strings with complex patterns, regular expressions are a powerful tool. Python’s re module provides facilities for matching strings to regex patterns.
Here’s an example:
import re
words = ["apple", "banana", "cherry", "date", "apricot"]
pattern = re.compile('ap')
filtered_words = [word for word in words if pattern.search(word)]
print(filtered_words)
Output:
['apple', 'apricot']
This snippet uses the re.compile() function to create a regex pattern object that is then used to search() within each word. The list comprehension filters out the words that do not match the pattern.
Bonus One-Liner Method 5: Using List Comprehension with startswith()
If you need to find strings that begin specifically with a given substring, you can use the startswith() method inside a list comprehension. This one-liner is short and clear if the match must be at the beginning of the string.
Here’s an example:
words = ["apple", "banana", "cherry", "date", "apricot"] substring = "ap" filtered_words = [word for word in words if word.startswith(substring)] print(filtered_words)
Output:
['apple', 'apricot']
Here, the list comprehension filters the list of words using the string method startswith(), which returns True only if a word starts with the specified substring.
Summary/Discussion
- Method 1: List Comprehension with
in. A succinct and Pythonic approach. It may not offer the best performance on very large lists due to memory considerations. - Method 2:
filter()with Lambda. Functional style that’s clean and clear but lacks the straightforwardness of a list comprehension for many Python users. - Method 3: Function with
filter(). It centralizes the filtering logic but introduces additional complexity by requiring the definition of an external function. - Method 4: Using Regex with
re. Best for complex patterns. Potentially overkill for simple substring checks and may be less performant for large datasets. - Method 5: List Comprehension with
startswith(). Best used when the substring needs to match from the beginning of the strings. It is less flexible than a general substring match.