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BigCodeBench / BigCodeBench v0.1.0_hf a30f1971-b990-5349-9038-27d34cd223b3

Problem

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

complete prompt

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


def task_func(a, b, columns=['A', 'B']):
    """
    Standardize two lists of numbers using the StandardScaler from sklearn and visualize the standardized values using a bar plot.

    Parameters:
        a (list): A list of numbers.
        b (list): Another list of numbers.
        columns (list, optional): Column names for the resulting DataFrame. Defaults to ['A', 'B'].

    Returns:
        pd.DataFrame: A DataFrame containing the standardized values.
        matplotlib.axes.Axes: Axes object of the displayed bar plot.

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

    Example:
        >>> df, ax = task_func([1, 2, 3, 4, 5], [2, 4, 6, 8, 10])
        >>> isinstance(df, pd.DataFrame) and isinstance(ax, matplotlib.axes.Axes)
        True
    """

instruct prompt

Standardize two lists of numbers using the StandardScaler from sklearn and visualize the standardized values using a bar plot.
The function should output with:
    pd.DataFrame: A DataFrame containing the standardized values.
    matplotlib.axes.Axes: Axes object of the displayed bar plot.
You should write self-contained code starting with:

Code

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
def task_func(a, b, columns=['A', 'B']):

code prompt

Code

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
def task_func(a, b, columns=['A', 'B']):

entry point

task_func

libs

  • pandas
  • numpy
  • matplotlib
  • sklearn

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Source and history

Official source

initial import