BigCodeBench / BigCodeBench v0.1.0_hf 08eb254d-8bf7-5d7f-b958-56e88018ff53

complete prompt

import pandas as pd
import regex as re
from sklearn.feature_extraction.text import CountVectorizer


def task_func(text):
    """
    Analyze a text by creating a document term matrix with CountVectorizer. The text contains several sentences, each separated by a period.
    Ignore empty sentences.

    Parameters:
    text (str): The text to analyze.

    Returns:
    DataFrame: A pandas DataFrame with the document-term matrix. Its column names should be adapted from the vectorizer feature names.

    Requirements:
    - pandas
    - regex
    - sklearn.feature_extraction.text.CountVectorizer

    Example:
    >>> text = "This is a sample sentence. This sentence contains sample words."
    >>> dtm = task_func(text)
    >>> print(dtm)
       contains  is  sample  sentence  this  words
    0         0   1       1         1     1      0
    1         1   0       1         1     1      1
    """

instruct prompt

Analyze a text by creating a document term matrix with CountVectorizer. The text contains several sentences, each separated by a period. Ignore empty sentences.
The function should output with:
    DataFrame: A pandas DataFrame with the document-term matrix. Its column names should be adapted from the vectorizer feature names.
You should write self-contained code starting with:

Code

import pandas as pd
import regex as re
from sklearn.feature_extraction.text import CountVectorizer
def task_func(text):

code prompt

Code

import pandas as pd
import regex as re
from sklearn.feature_extraction.text import CountVectorizer
def task_func(text):

entry point

task_func

libs

• regex
• pandas
• sklearn