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BigCodeBench / BigCodeBench v0.1.0_hf 0bb7871f-0c44-570b-85ce-f0ab6ddd9fb2
Problem
Answer published by the source. Consult the official source to check your work against its answer.
complete prompt
import pandas as pd
import random
import csv
def task_func(n,
categories=['Sports', 'Technology', 'Business', 'Politics', 'Entertainment'],
news_sites=['New York Times', 'USA Today', 'Apple News', 'CNN', 'BBC'],
likert_scale=['Strongly Disagree', 'Disagree', 'Neither Agree nor Disagree', 'Agree', 'Strongly Agree'],
file_path='news_survey_data.csv',
random_seed=None):
"""
Generate a DataFrame with random survey data based on given categories,
news sites, and Likert scale responses. The function writes the generated
data to a CSV file and then reads it into a Pandas DataFrame.
Parameters:
n (int): The number of survey responses to generate.
categories (list, optional): Categories of news to choose from. Defaults to ['Sports', 'Technology', 'Business', 'Politics', 'Entertainment'].
news_sites (list, optional): News sites to choose from. Defaults to ['New York Times', 'USA Today', 'Apple News', 'CNN', 'BBC'].
likert_scale (list, optional): Likert scale responses to choose from. Defaults to ['Strongly Disagree', 'Disagree', 'Neither Agree nor Disagree', 'Agree', 'Strongly Agree'].
file_path (str, optional): Path to save the generated CSV file. Defaults to 'news_survey_data.csv'.
random_seed (int): Seed for rng. Used for generating datapoints. Defaults to None.
Returns:
DataFrame: A pandas DataFrame with columns ['Site', 'Category', 'Response', 'Value'].
The 'Value' column assigns a numerical value to the Likert scale response (starting from 1).
Requirements:
- pandas
- random
- csv
Example:
>>> df = task_func(5, random_seed=1)
>>> print(df)
Site Category Response Value
0 USA Today Entertainment Strongly Disagree 1
1 Apple News Sports Agree 4
2 CNN Politics Agree 4
3 USA Today Sports Agree 4
4 New York Times Politics Agree 4
>>> df = task_func(8, ['test', 'fun'], likert_scale=['true', 'false'], news_sites=['cat', 'dog'], random_seed=12)
>>> print(df)
Site Category Response Value
0 dog fun False 2
1 cat fun True 1
2 dog fun False 2
3 dog test True 1
4 cat fun False 2
5 cat fun True 1
6 cat test True 1
7 dog fun True 1
"""
instruct prompt
Generate a DataFrame with random survey data based on given categories, news sites, and Likert scale responses. The function writes the generated data to a CSV file and then reads it into a Pandas DataFrame. >>> df = task_func(8, ['test', 'fun'], likert_scale=['true', 'false'], news_sites=['cat', 'dog'], random_seed=12) >>> print(df) Site Category Response Value 0 dog fun False 2 1 cat fun True 1 2 dog fun False 2 3 dog test True 1 4 cat fun False 2 5 cat fun True 1 6 cat test True 1 7 dog fun True 1
The function should output with:
DataFrame: A pandas DataFrame with columns ['Site', 'Category', 'Response', 'Value'].
The 'Value' column assigns a numerical value to the Likert scale response (starting from 1).
You should write self-contained code starting with:
Code
import pandas as pd
import random
import csv
def task_func(n,
categories=['Sports', 'Technology', 'Business', 'Politics', 'Entertainment'],
news_sites=['New York Times', 'USA Today', 'Apple News', 'CNN', 'BBC'],
likert_scale=['Strongly Disagree', 'Disagree', 'Neither Agree nor Disagree', 'Agree', 'Strongly Agree'],
file_path='news_survey_data.csv',
random_seed=None):
code prompt
Code
import pandas as pd
import random
import csv
def task_func(n,
categories=['Sports', 'Technology', 'Business', 'Politics', 'Entertainment'],
news_sites=['New York Times', 'USA Today', 'Apple News', 'CNN', 'BBC'],
likert_scale=['Strongly Disagree', 'Disagree', 'Neither Agree nor Disagree', 'Agree', 'Strongly Agree'],
file_path='news_survey_data.csv',
random_seed=None):
entry point
task_func
libs
- pandas
- csv
- random
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initial import