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BigCodeBench / BigCodeBench v0.1.0_hf d339b555-d60d-50d2-818e-d6923a95c81f

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import pandas as pd
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

# Constants
COLUMNS = ['Date', 'Value']

def task_func(df, plot=False):
    '''
    Splits a list in the 'Value' column of a DataFrame into several columns, scales these columns using StandardScaler, 
    and optionally returned the scaled data using a bar chart. The 'Date' column is converted to datetime and used as 
    the index in the plot.

    Parameters:
    df (DataFrame): A pandas DataFrame with a 'Date' column and a 'Value' column where 'Value' contains lists of numbers.
    plot (bool): If True, a bar chart of the scaled values is displayed. Defaults to False.

    Returns:
    DataFrame: A pandas DataFrame with the 'Date' column and additional columns for each element in the original 'Value' list,
               where these columns contain the scaled values.
    Axes (optional): A matplotlib Axes object containing the bar chart, returned if 'plot' is True.

    Note:
    - This function use "Scaled Values Over Time" for the plot title.
    - This function use "Date" and "Scaled Value" as the xlabel and ylabel respectively.

    Raises:
    - This function will raise KeyError if the DataFrame does not have the 'Date' and 'Value' columns.

    Requirements:
    - pandas
    - sklearn.preprocessing.StandardScaler
    - matplotlib.pyplot

    Example:
    >>> df = pd.DataFrame([['2021-01-01', [8, 10, 12]], ['2021-01-02', [7, 9, 11]]], columns=COLUMNS)
    >>> scaled_df, ax = task_func(df, plot=True)
    >>> print(scaled_df.shape)
    (2, 4)
    >>> plt.close()
    '''

instruct prompt

Splits a list in the 'Value' column of a DataFrame into several columns, scales these columns using StandardScaler, and optionally returned the scaled data using a bar chart. The 'Date' column is converted to datetime and used as the index in the plot.
Note that: This function use "Scaled Values Over Time" for the plot title. This function use "Date" and "Scaled Value" as the xlabel and ylabel respectively.
The function should raise the exception for: This function will raise KeyError if the DataFrame does not have the 'Date' and 'Value' columns.
The function should output with:
    DataFrame: A pandas DataFrame with the 'Date' column and additional columns for each element in the original 'Value' list,
    where these columns contain the scaled values.
    Axes (optional): A matplotlib Axes object containing the bar chart, returned if 'plot' is True.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
# Constants
COLUMNS = ['Date', 'Value']
def task_func(df, plot=False):

code prompt

Code

import pandas as pd
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
# Constants
COLUMNS = ['Date', 'Value']
def task_func(df, plot=False):

entry point

task_func

libs

  • pandas
  • matplotlib
  • sklearn

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Source and history

task_id
d339b555-d60d-50d2-818e-d6923a95c81f
task_key
default--v0~2e1~2e0~5fhf--d339b555-d60d-50d2-818e-d6923a95c81f
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import