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The dataset generation failed
Error code: DatasetGenerationError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
task_id string | solution string | raw_solution string |
|---|---|---|
BigCodeBench/13 | import subprocess
import ftplib
import os
def task_func(ftp_server='ftp.dlptest.com', ftp_user='dlpuser', ftp_password='rNrKYTX9g7z3RgJRmxWuGHbeu', ftp_dir='/ftp/test'):
"""
Download all files from a specific directory on an FTP server using wget in a subprocess.
Args:
ftp_server (str): The FTP ser... | import subprocess
import ftplib
import os
def task_func(ftp_server='ftp.dlptest.com', ftp_user='dlpuser', ftp_password='rNrKYTX9g7z3RgJRmxWuGHbeu', ftp_dir='/ftp/test'):
"""
Download all files from a specific directory on an FTP server using wget in a subprocess.
Args:
ftp_server (str): The FTP se... |
BigCodeBench/15 | import subprocess
import csv
import os
def task_func(commands_file_path, output_dir_path):
"""
Execute a list of shell commands read from a CSV file and save the outputs in separate files.
Each command's output is written to a unique file in the specified output directory.
If a command fails, the error ... | import subprocess
import csv
import os
def task_func(commands_file_path, output_dir_path):
"""
Execute a list of shell commands read from a CSV file and save the outputs in separate files.
Each command's output is written to a unique file in the specified output directory.
If a command fails, the error... |
BigCodeBench/17 | import subprocess
import psutil
import time
def task_func(process_name: str) -> str:
'''
Check if a particular process is running based on its name. If it is not running, start it using the process name as a command.
If it is running, terminate the process and restart it by executing the process name as a ... | import subprocess
import psutil
import time
def task_func(process_name: str) -> str:
'''
Check if a particular process is running based on its name. If it is not running, start it using the process name as a command.
If it is running, terminate the process and restart it by executing the process name as a... |
BigCodeBench/19 | import os
import glob
import zipfile
def task_func(directory):
"""
Zips all files (not including subdirectories) located in the specified directory and returns the path to the created zip file.
Parameters:
directory (str): The directory path containing the files to be zipped.
Returns:
... | import os
import glob
import zipfile
def task_func(directory):
"""
Zips all files (not including subdirectories) located in the specified directory and returns the path to the created zip file.
Parameters:
directory (str): The directory path containing the files to be zipped.
Returns:
... |
BigCodeBench/34 | import re
from wordcloud import WordCloud
import matplotlib.pyplot as plt
def task_func(text):
"""
Create a word cloud from text after removing URLs and plot it.
Parameters:
- text (str): The text to analyze.
Returns:
WordCloud object: The generated word cloud.
Raises:
ValueError("No w... | import re
from wordcloud import WordCloud
import matplotlib.pyplot as plt
def task_func(text):
"""
Create a word cloud from text after removing URLs and plot it.
Parameters:
- text (str): The text to analyze.
Returns:
WordCloud object: The generated word cloud.
Raises:
ValueError("No... |
BigCodeBench/37 | import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
def task_func(df, target_column):
"""
Train a random forest classifier to perform the classification of the rows in a dataframe with respect to the column of interest... | import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import seaborn as sns
import matplotlib.pyplot as plt
def task_func(df, target_column):
"""
Train a random forest classifier to perform the classification of the rows in a dataframe with respect to the column of interest plot the bar plot ... |
BigCodeBench/82 | from flask import Flask, render_template, redirect, url_for
from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user
from flask_wtf import FlaskForm
from wtforms import StringField, PasswordField, SubmitField
from wtforms.validators import DataRequired, Length
from werkzeug... | from flask import Flask, render_template, redirect, url_for
from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user
from flask_wtf import FlaskForm
from wtforms import StringField, PasswordField, SubmitField
from wtforms.validators import DataRequired, Length
from werkzeug... |
BigCodeBench/89 | import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.preprocessing import StandardScaler
def task_func(data, column, outlier_z_score):
"""
Identifies and removes outliers from a specified column of a dataset based on the Z-score.
It standardizes the column, calculates Z-sc... | import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.preprocessing import StandardScaler
def task_func(data, column, outlier_z_score):
"""
Identifies and removes outliers from a specified column of a dataset based on the Z-score.
It standardizes the column, calculates Z-s... |
BigCodeBench/92 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from matplotlib.collections import PathCollection
def task_func(data, n_clusters=3):
"""
Perform K-means clustering on a dataset and generate a scatter plot visualizing the clusters and their centroids.
... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from matplotlib.collections import PathCollection
def task_func(data, n_clusters=3):
"""
Perform K-means clustering on a dataset and generate a scatter plot visualizing the clusters and their centroids.
... |
BigCodeBench/93 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
def task_func(data, n_components=2):
"""
Perform Principal Component Analysis (PCA) on a dataset and record the result.
Also, generates a scatter plot of the transformed data.
Parameters:
da... | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
def task_func(data, n_components=2):
"""
Perform Principal Component Analysis (PCA) on a dataset and record the result.
Also, generates a scatter plot of the transformed data.
Parameters:
d... |
BigCodeBench/99 | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_iris
def task_func():
"""
Draws a seaborn pair plot of the iris dataset using Arial font.
This function sets the global font to Arial for better readability and visual appeal. It then generates a pai... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_iris
def task_func():
"""
Draws a seaborn pair plot of the iris dataset using Arial font.
This function sets the global font to Arial for better readability and visual appeal. It then generates a pa... |
BigCodeBench/100 | import matplotlib.pyplot as plt
import pandas as pd
import random
from datetime import datetime
def task_func(seed=42):
"""
Generates a plot of random time series data for the past 30 days with reproducibility
controlled by an optional seed parameter.
The plot is styled with Arial font for better read... | import matplotlib.pyplot as plt
import pandas as pd
import random
from datetime import datetime
def task_func(seed=42):
"""
Generates a plot of random time series data for the past 30 days with reproducibility
controlled by an optional seed parameter.
The plot is styled with Arial font for better rea... |
BigCodeBench/101 | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import os
def task_func(data_url="http://lib.stat.cmu.edu/datasets/boston", seed=42):
"""
Draw the correlation heatmap of the Boston Housing dataset using Seaborn, with an option to save it to a specified file.
Par... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import os
def task_func(data_url="http://lib.stat.cmu.edu/datasets/boston", seed=42):
"""
Draw the correlation heatmap of the Boston Housing dataset using Seaborn, with an option to save it to a specified file.
Pa... |
BigCodeBench/108 | import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose
def task_func(df, freq='D', decomposition_model='multiplicative'):
"""
Decomposes a time series in the 'value' column of a DataFrame into trend, seasonality, and residuals.
Parameters:
df (DataFr... | import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose
def task_func(df, freq='D', decomposition_model='multiplicative'):
"""
Decomposes a time series in the 'value' column of a DataFrame into trend, seasonality, and residuals.
Parameters:
df (DataF... |
BigCodeBench/120 | import pandas as pd
from datetime import datetime, timedelta
from random import randint, seed as random_seed
def task_func(start_date=datetime(2020, 1, 1), end_date=datetime(2020, 12, 31), seed=42):
"""
Generate a pandas Series of random dates within a specified date range,
including both start_date and en... | import pandas as pd
from datetime import datetime, timedelta
from random import randint, seed as random_seed
def task_func(start_date=datetime(2020, 1, 1), end_date=datetime(2020, 12, 31), seed=42):
"""
Generate a pandas Series of random dates within a specified date range,
including both start_date and e... |
BigCodeBench/123 | import pandas as pd
import os
import glob
def task_func(my_list, file_dir='./data_files/', file_ext='.csv'):
"""
Modify a list by adding the element '12', then concatenate a number of CSV files
from a directory into a single DataFrame. The number of files concatenated is
determined by the sum of the n... | import pandas as pd
import os
import glob
def task_func(my_list, file_dir='./data_files/', file_ext='.csv'):
"""
Modify a list by adding the element '12', then concatenate a number of CSV files
from a directory into a single DataFrame. The number of files concatenated is
determined by the sum of the ... |
BigCodeBench/124 | from random import randint, seed as random_seed
import time
import matplotlib.pyplot as plt
def task_func(my_list, size=100, seed=100):
"""
Enhances 'my_list' by appending the number 12, then generates a list of random integers based
on the sum of elements in 'my_list', limited by 'size'. It measures the t... | from random import randint, seed as random_seed
import time
import matplotlib.pyplot as plt
def task_func(my_list, size=100, seed=100):
"""
Enhances 'my_list' by appending the number 12, then generates a list of random integers based
on the sum of elements in 'my_list', limited by 'size'. It measures the ... |
BigCodeBench/129 | import requests
from bs4 import BeautifulSoup
import pandas as pd
def task_func(url='http://example.com'):
"""
Scrape the first table from a web page and extract data into a Pandas DataFrame.
This function scrapes the first table found on the specified web page URL and extracts the data into a DataFrame,
... | import requests
from bs4 import BeautifulSoup
import pandas as pd
def task_func(url='http://example.com'):
"""
Scrape the first table from a web page and extract data into a Pandas DataFrame.
This function scrapes the first table found on the specified web page URL and extracts the data into a DataFrame,
... |
BigCodeBench/139 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def task_func(df):
"""
Draw histograms of numeric columns in a DataFrame and return the plots.
Each histogram represents the distribution of values in one numeric column,
with the column name as the plot title, 'Value' as the x-axis... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def task_func(df):
"""
Draw histograms of numeric columns in a DataFrame and return the plots.
Each histogram represents the distribution of values in one numeric column,
with the column name as the plot title, 'Value' as the x-axi... |
BigCodeBench/147 | import socket
from ipaddress import IPv4Network
from threading import Thread
def task_func(ip_range, port):
"""
Scans a specified IP address range and checks if a specified port is open on each IP.
The function returns a dictionary with IP addresses as keys and a boolean indicating
the port's status (Tr... | import socket
from ipaddress import IPv4Network
from threading import Thread
def task_func(ip_range, port):
"""
Scans a specified IP address range and checks if a specified port is open on each IP.
The function returns a dictionary with IP addresses as keys and a boolean indicating
the port's status (T... |
BigCodeBench/161 | import re
import pandas as pd
from datetime import datetime
def task_func(log_file):
"""
Extracts logging information such as message type, timestamp, and the message itself from a log file and
stores the data in a CSV format. This utility is ideal for converting plain text logs into a more structured forma... | import re
import pandas as pd
from datetime import datetime
def task_func(log_file):
"""
Extracts logging information such as message type, timestamp, and the message itself from a log file and
stores the data in a CSV format. This utility is ideal for converting plain text logs into a more structured form... |
BigCodeBench/162 | import re
import matplotlib.pyplot as plt
import numpy as np
def task_func(text, rwidth=0.8):
"""
Analyzes and visualizes the distribution of word lengths in a text. The function generates a histogram subplot,
which facilitates the understanding of how word lengths vary within the provided text.
Parame... | import re
import matplotlib.pyplot as plt
import numpy as np
def task_func(text, rwidth=0.8):
"""
Analyzes and visualizes the distribution of word lengths in a text. The function generates a histogram subplot,
which facilitates the understanding of how word lengths vary within the provided text.
Param... |
BigCodeBench/177 | import re
import nltk
from string import punctuation
import pandas as pd
def task_func(df):
"""
Extracts articles whose titles contain specific case-insensitive keywords ("like" or "what") from a DataFrame and analyzes
the frequency of each word in the content of these articles, excluding punctuation.
... | import re
import nltk
from string import punctuation
def task_func(df):
"""
Extracts articles whose titles contain specific case-insensitive keywords ("like" or "what") from a DataFrame and analyzes
the frequency of each word in the content of these articles, excluding punctuation.
Parameters:
df ... |
BigCodeBench/184 | import pandas as pd
import re
from sklearn.feature_extraction.text import CountVectorizer
STOPWORDS = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself',
'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself',
... | import pandas as pd
import re
from sklearn.feature_extraction.text import CountVectorizer
# Constants
STOPWORDS = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself',
'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'it... |
BigCodeBench/187 | import numpy as np
import geopandas as gpd
from shapely.geometry import Point
def task_func(dic={'Lon': (-180, 180), 'Lat': (-90, 90)}, cities=['New York', 'London', 'Beijing', 'Tokyo', 'Sydney']):
"""
Create a GeoPandas DataFrame for a list of cities with randomly generated coordinates based on specified range... | import numpy as np
import geopandas as gpd
from shapely.geometry import Point
def task_func(dic={'Lon': (-180, 180), 'Lat': (-90, 90)}, cities=['New York', 'London', 'Beijing', 'Tokyo', 'Sydney']):
"""
Create a GeoPandas DataFrame for a list of cities with randomly generated coordinates based on specified rang... |
BigCodeBench/199 | import pandas as pd
import pytz
from datetime import datetime
from random import randint, seed as set_seed
def task_func(
utc_datetime,
cities=['New York', 'London', 'Beijing', 'Tokyo', 'Sydney'],
weather_conditions=['Sunny', 'Cloudy', 'Rainy', 'Snowy', 'Stormy'],
timezones={
'New York': 'Americ... | import pandas as pd
import pytz
from datetime import datetime
from random import randint, seed as set_seed
def task_func(
utc_datetime,
cities=['New York', 'London', 'Beijing', 'Tokyo', 'Sydney'],
weather_conditions=['Sunny', 'Cloudy', 'Rainy', 'Snowy', 'Stormy'],
timezones={
'New York': 'Ameri... |
BigCodeBench/208 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def task_func(elements, seed=0):
"""
Generate and draw a random sequence of "elements" number of steps. The steps are either
-1 or 1, and the sequence is plotted as a random walk. Returns the descriptive statistics
of the random wa... | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def task_func(elements, seed=0):
"""
Generate and draw a random sequence of "elements" number of steps. The steps are either
-1 or 1, and the sequence is plotted as a random walk. Returns the descriptive statistics
of the random w... |
BigCodeBench/211 | import requests
import os
import zipfile
def task_func(url, destination_directory, headers=None):
"""
Download and keep a zip file from a URL, extract its contents to the specified directory, and return the list of extracted files.
Parameters:
url (str): The URL of the zip file to download.
destina... | import requests
import os
import zipfile
def task_func(url, destination_directory, headers=None):
"""
Download and keep a zip file from a URL, extract its contents to the specified directory, and return the list of extracted files.
Parameters:
url (str): The URL of the zip file to download.
destin... |
BigCodeBench/214 | import random
import numpy as np
import cv2
import matplotlib.pyplot as plt
def task_func(seed=42, image_size=(100, 100, 3), range_low=0, range_high=255):
"""
Generate a random RGB image and view it.
Parameters:
- seed (int, optional): Random seed for reproducibility. Default is 42.
- image_size (t... | import random
import numpy as np
import cv2
import matplotlib.pyplot as plt
def task_func(seed=42, image_size=(100, 100, 3), range_low=0, range_high=255):
"""
Generate a random RGB image and view it.
Parameters:
- seed (int, optional): Random seed for reproducibility. Default is 42.
- image_size (... |
BigCodeBench/227 | import numpy as np
import os
import soundfile as sf
import librosa
import matplotlib.pyplot as plt
def task_func(L, M, N, audio_file):
"""
Creates an MxN matrix from a list L, normalizes it based on the sound pressure level
(SPL) of a specified audio file, and generates a spectrogram from the matrix.
P... | import numpy as np
import os
import soundfile as sf
import librosa
import matplotlib.pyplot as plt
def task_func(L, M, N, audio_file):
"""
Creates an MxN matrix from a list L, normalizes it based on the sound pressure level
(SPL) of a specified audio file, and generates a spectrogram from the matrix.
... |
BigCodeBench/239 | import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
def task_func(original):
"""
Given a list of tuples, extract numeric values, compute basic statistics, and
generate a histogram with an overlaid probability density function (PDF).
Parameters:
original (list of tuples): Inp... | import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
def task_func(original):
"""
Given a list of tuples, extract numeric values, compute basic statistics, and
generate a histogram with an overlaid probability density function (PDF).
Parameters:
original (list of tuples): In... |
BigCodeBench/241 | import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
def task_func(original):
"""
Create a numeric array from the "original" list, normalize the array, and draw the original and normalized arrays.
The function will plot the original and normalized arrays with a title of ... | import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
def task_func(original):
"""
Create a numeric array from the "original" list, normalize the array, and draw the original and normalized arrays.
The function will plot the original and normalized arrays with a title o... |
BigCodeBench/267 | import numpy as np
from scipy import fftpack
import matplotlib.pyplot as plt
def task_func(data, sample_rate=8000):
"""
Given a dictionary "data", this function performs the following operations:
1. Adds a new key "a" with the value 1 to the dictionary.
2. Generates a signal based on the values in "data... | import numpy as np
from scipy import fftpack
import matplotlib.pyplot as plt
def task_func(data, sample_rate=8000):
"""
Given a dictionary "data", this function performs the following operations:
1. Adds a new key "a" with the value 1 to the dictionary.
2. Generates a signal based on the values in "dat... |
BigCodeBench/273 | import cgi
import http.server
import json
SUCCESS_RESPONSE = {
'status': 'success',
'message': 'Data received successfully.'
}
ERROR_RESPONSE = {
'status': 'error',
'message': 'Invalid data received.'
}
def task_func():
"""
Creates an HTTP POST request handler for processing incoming data. The d... | import cgi
import http.server
import json
SUCCESS_RESPONSE = {
'status': 'success',
'message': 'Data received successfully.'
}
ERROR_RESPONSE = {
'status': 'error',
'message': 'Invalid data received.'
}
def task_func():
"""
Creates an HTTP POST request handler for processing incoming data. Th... |
BigCodeBench/274 | import cgi
import http.server
import smtplib
from email.mime.text import MIMEText
import json
def task_func(smtp_server, smtp_port, smtp_username, smtp_password):
"""
Creates an HTTP POST request handler that processes incoming email data and sends
an email. The email data must be a JSON object with 'subjec... | import cgi
import http.server
import smtplib
from email.mime.text import MIMEText
import json
def task_func(smtp_server, smtp_port, smtp_username, smtp_password):
"""
Creates an HTTP POST request handler that processes incoming email data and sends
an email. The email data must be a JSON object with 'subje... |
BigCodeBench/287 | from collections import Counter
import os
import json
def task_func(filename, directory):
"""
Count the number of words in .txt files within a specified directory,
export the counts to a JSON file, and then return the total number of words.
Parameters:
filename (str): The name of the output JSON f... | from collections import Counter
import os
import json
def task_func(filename, directory):
"""
Count the number of words in .txt files within a specified directory,
export the counts to a JSON file, and then return the total number of words.
Parameters:
filename (str): The name of the output JSON ... |
BigCodeBench/302 | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def task_func(df, plot=False):
'''
Processes a pandas DataFrame by splitting lists in the 'Value' column into separate columns,
calculates the Pearson correlation coefficient between these columns, and optionally visualizes
the ... | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Constants
COLUMNS = ['Date', 'Value']
def task_func(df, plot=False):
'''
Processes a pandas DataFrame by splitting lists in the 'Value' column into separate columns,
calculates the Pearson correlation coefficient between these co... |
BigCodeBench/308 | import pandas as pd
from statistics import mean
import random
FIELDS = ['Physics', 'Math', 'Chemistry', 'Biology', 'English', 'History']
def task_func(additional_fields = []):
"""
Create a report on students' grades in different subjects and then calculate the average grade for each student and subject.
... | import pandas as pd
from statistics import mean
import random
# Constants for generating the report data
FIELDS = ['Physics', 'Math', 'Chemistry', 'Biology', 'English', 'History']
STUDENTS = ['Student_' + str(i) for i in range(1, 101)]
def task_func(additional_fields = []):
"""
Create a report on students' gr... |
BigCodeBench/310 | import os
import csv
import random
from statistics import mean
COLUMNS = ['Name', 'Age', 'Height', 'Weight']
PEOPLE_COUNT = 100
def task_func(filename):
"""
Generates a CSV file containing simulated data for 100 people, including name, age, height, and weight.
It also calculates and appends the average age... | import os
import csv
import random
from statistics import mean
# Constants
COLUMNS = ['Name', 'Age', 'Height', 'Weight']
PEOPLE_COUNT = 100
def task_func(filename):
"""
Generates a CSV file containing simulated data for 100 people, including name, age, height, and weight.
It also calculates and appends t... |
BigCodeBench/313 | import re
import os
import shutil
from datetime import datetime
def task_func(directory):
"""
Organize files in a directory based on the first text that is not enclosed in square brackets.
Move the files to subdirectories named after this text. If no matching text is found,
the file is not moved.
P... | import re
import os
import shutil
from datetime import datetime
def task_func(directory):
"""
Organize files in a directory based on the first text that is not enclosed in square brackets.
Move the files to subdirectories named after this text. If no matching text is found,
the file is not moved.
... |
BigCodeBench/324 | import subprocess
import time
import threading
def task_func(file_list):
"""
Run files from list of files as subprocesses at the same time.
Parameters:
- file_list (list of str): List of files name to run.
Returns:
list: The exit codes of the subprocesses.
Requirements:
- subproce... | import subprocess
import time
import threading
def task_func(file_list):
"""
Run files from list of files as subprocesses at the same time.
Parameters:
- file_list (list of str): List of files name to run.
Returns:
list: The exit codes of the subprocesses.
Requirements:
- subpro... |
BigCodeBench/326 | import subprocess
import os
import sys
import glob
def task_func(directory_path):
"""
Find and run all .bat files in a given directory, returning their file names and exit codes.
Parameters:
directory_path (str): The path of the directory to search for .bat files.
Returns:
list of tuples: A li... | import subprocess
import os
import sys
import glob
def task_func(directory_path):
"""
Find and run all .bat files in a given directory, returning their file names and exit codes.
Parameters:
directory_path (str): The path of the directory to search for .bat files.
Returns:
list of tuples: A l... |
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