BigCodeBench / BigCodeBench v0.1.0_hf 43b82ea5-6e4b-5c9a-9257-dd661a33f3ba

complete prompt

import re
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer


def task_func(df):
    """
    Analyzes articles by their titles for specific case-insensitive keywords ("how" or "what"), vectorizes the content using
    CountVectorizer, and groups them into clusters using KMeans clustering. This function is intended for basic
    content analysis and clustering to understand common themes or topics among articles asking questions starting
    with "how" or "what".

    Parameters:
    df (pd.DataFrame): DataFrame containing article data with columns 'Title' for the article titles and 'Content' for
    the article text.

    Returns:
    list: List of cluster labels for the filtered articles, indicating the cluster to which each article belongs.

    Requirements:
    - re
    - sklearn

    Example:
    >>> import pandas as pd
    >>> df_sample = pd.DataFrame({
    ...    'Title': ['How to code?', 'What is Python?', 'The art of programming', 'How to cook?', 'What is life?'],
    ...    'Content': ['This is a tutorial about coding...', 'Python is a programming language...',
    ...                'Programming is an art...', 'This is a cooking tutorial...', 'Life is complicated...']
    ... })
    >>> task_func(df_sample)
    [0, 1, 0, 1]
    """

instruct prompt

Analyzes articles by their titles for specific case-insensitive keywords ("how" or "what"), vectorizes the content using CountVectorizer, and groups them into clusters using KMeans clustering. This function is intended for basic content analysis and clustering to understand common themes or topics among articles asking questions starting with "how" or "what".
The function should output with:
    list: List of cluster labels for the filtered articles, indicating the cluster to which each article belongs.
You should write self-contained code starting with:

Code

import re
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
def task_func(df):

code prompt

Code

import re
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
def task_func(df):

entry point

task_func

libs

• re
• sklearn