BigCodeBench / BigCodeBench v0.1.0_hf cca02587-373d-5f06-9aa2-49740c274a9e

complete prompt

from scipy.stats import zscore
import matplotlib.pyplot as plt


def task_func(df):
    """
    Calculate Z-scores for numeric columns in a DataFrame and draw a histogram for each column.
    - Missing values are replaced by the column's average.
    - The histograms are plotted with 10 bins.

    Parameters:
    - df (pandas.DataFrame): The input pandas DataFrame with numeric columns.

    Returns:
    - tuple:
        1. pandas.DataFrame: A DataFrame with computed z-scores.
        2. list: A list of Axes objects representing the histograms of the numeric columns.

    Requirements:
    - pandas.
    - numpy.
    - scipy.stats.zscore.
    - matplotlib.pyplot.

    Example:
    >>> import pandas as pd
    >>> import numpy as np
    >>> df_input = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7.0, np.nan, 9.0]], columns=["col1", "col2", "col3"])
    >>> zscore_output, plots = task_func(df_input)
    """

instruct prompt

Calculate Z-scores for numeric columns in a DataFrame and draw a histogram for each column. - Missing values are replaced by the column's average. - The histograms are plotted with 10 bins.
The function should output with:
    tuple:
    1. pandas.DataFrame: A DataFrame with computed z-scores.
    2. list: A list of Axes objects representing the histograms of the numeric columns.
You should write self-contained code starting with:

Code

from scipy.stats import zscore
import matplotlib.pyplot as plt
def task_func(df):

code prompt

Code

from scipy.stats import zscore
import matplotlib.pyplot as plt
def task_func(df):

entry point

task_func

libs

• matplotlib
• scipy