5 Best Ways to Find Programming Questions in a String with Python

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5 Best Ways to Find Programming Questions in a String with Python

πŸ’‘ Problem Formulation: Programmers often need to extract questions or problem statements from a larger corpus of text. In Python, various methods can be used to identify these questions. For instance, given the input text “What is the output of this code? def hello_world(): print(‘Hello, World!’)” we want to extract the string “What is the output of this code?”.

Method 1: Using String Methods

Simple Python string methods can be effective for extracting questions from a text block. This method involves searching for interrogation marks and substrings denoting a question. It is straightforward and requires no external libraries.

Here’s an example:

def find_question(text):
    if '?' in text:
        question_end = text.index('?') + 1
        return text[:question_end]
    return "No question found."

text = "How many Python programmers does it take to change a light bulb? None, that's a hardware problem."

Output: "How many Python programmers does it take to change a light bulb?"

This code searches for the ‘?’ character in the text to mark the end of a question. It slices the string up to and including the interrogation point to extract the question. However, it can only extract the first question and may be simplistic for complex texts.

Method 2: Regular Expressions

Regular expressions allow for pattern matching, which can be tailored to match the syntactic pattern of questions. This method is powerful and flexible and can be adjusted to the complexity of the language within the text.

Here’s an example:

import re

def find_questions(text):
    return re.findall(r'([^.]*\?)[^\w]', text)

text = "Is this the most efficient code? Maybe. Can it be improved? Definitely!"
questions = find_questions(text)

Output: ['Is this the most efficient code?', 'Can it be improved?']

This code uses regular expressions to find all occurrences of text ending with a ‘?’. The pattern rx'([^.]*\?)[^\w]' captures groups of characters that do not include a period (.) and end with a question mark, making this method capable of retrieving multiple questions from a single string.

Method 3: Natural Language Processing (NLP)

Natural Language Processing can be used to deeply analyze text structure. Using libraries such as spaCy or NLTK, Python can parse sentences and identify which are questions based on their structure and punctuation. This is a sophisticated method that can handle complex questions.

Here’s an example:

import spacy

nlp = spacy.load('en_core_web_sm')

def find_questions(text):
    doc = nlp(text)
    return [sent.text for sent in doc.sents if sent.text.strip().endswith('?')]

text = "Who wrote this? Was it you? This is impressive."
questions = find_questions(text)

Output: ['Who wrote this?', 'Was it you?']

This snippet utilizes the spaCy library to segment the text into sentences and then filters out sentences that are questions. NLP libraries are well-suited for complex language tasks, but this method may be slower and requires the additional overhead of loading language models.

Method 4: Keyword Search

For text data with patterns or specific technical keywords, a search based on these keywords could effectively extract questions. This approach can be ideal when you know the structure of the questions you are looking for.

Here’s an example:

def find_questions_by_keyword(text, keyword):
    sentences = text.split('.')
    questions = [sentence + '.' for sentence in sentences if keyword in sentence and '?' in sentence]
    return questions

text = "Why does the function fail? It's because the variable is undefined. Where is the variable defined?"
questions = find_questions_by_keyword(text, 'variable')

Output: ['Why does the function fail?', 'Where is the variable defined?']

In this method, the code looks for sentences containing both a keyword of interest and a question mark, effectively extracting sentences pertaining to a specific topic. It’s efficient for targeted searches but less generalizable than other methods.

Bonus One-Liner Method 5: List Comprehension with String Methods

A one-liner technique using list comprehension and string methods provides a quick and easy solution for extracting questions. This method is succinct and can be written in a single line of code, but it is limited by the simplicity of string methods.

Here’s an example:

text = "When is the deadline? It's soon. Are there any extensions possible? Unlikely."
questions = [sentence + '?' for sentence in text.split('?') if '?' in sentence]

Output: ['When is the deadline?', 'Are there any extensions possible?']

This snippet splits the text at question marks and re-adds the question mark to the sentences that contained them. While being extremely concise, it is very simplistic and assumes that questions end directly before a question mark and the next sentence does not immediately follow the ‘?’.


  • Method 1: String Methods. Easy to implement. Limited to simple cases and retrieving single questions.
  • Method 2: Regular Expressions. Highly flexible and capable. Potentially complex and may require fine-tuning for nuanced text.
  • Method 3: Natural Language Processing. Sophisticated language understanding. Slower performance due to model loading and processing.
  • Method 4: Keyword Search. Great for targeted question extraction. Not generalized for all types of questions.
  • Method 5: One-Liner List Comprehension. Quick and concise. May lack sophistication in handling text nuances.