BigCodeBench / BigCodeBench v0.1.0_hf 9d02d105-331b-513d-b37a-253474fea2f4

complete prompt

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

# Constants
FEATURE_NAMES = ["Feature 1", "Feature 2", "Feature 3", "Feature 4", "Feature 5"]


def task_func(data_matrix):
    """
    Standardize a 2D data matrix, calculate the mean value of each row and then visualize the distribution of the mean values with an histogram.
    - Each row of the matrix represent a data point, its length is the same as that of FEATURE_NAMES.
    - The plot title should be 'Distribution of Means'.

    Parameters:
    data_matrix (numpy.array): The 2D data matrix.

    Returns:
    tuple: A tuple containing:
        - pandas.DataFrame: A DataFrame containing the standardized data and the mean of each row.
                            Its column names should be FEATURE_NAMES and 'Mean'.
        - matplotlib.axes.Axes: The histogram plot of the distribution of means.

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

    Example:
    >>> import numpy as np
    >>> data = np.array([[6, 8, 1, 3, 4], [-1, 0, 3, 5, 1]])
    >>> df, ax = task_func(data)
    >>> print(df)
       Feature 1  Feature 2  Feature 3  Feature 4  Feature 5  Mean
    0        1.0        1.0       -1.0       -1.0        1.0   0.2
    1       -1.0       -1.0        1.0        1.0       -1.0  -0.2
    """

instruct prompt

Standardize a 2D data matrix, calculate the mean value of each row and then visualize the distribution of the mean values with an histogram. - Each row of the matrix represent a data point, its length is the same as that of FEATURE_NAMES. - The plot title should be 'Distribution of Means'.
The function should output with:
    tuple: A tuple containing:
    pandas.DataFrame: A DataFrame containing the standardized data and the mean of each row.
    Its column names should be FEATURE_NAMES and 'Mean'.
    matplotlib.axes.Axes: The histogram plot of the distribution of means.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
# Constants
FEATURE_NAMES = ["Feature 1", "Feature 2", "Feature 3", "Feature 4", "Feature 5"]
def task_func(data_matrix):

code prompt

Code

import pandas as pd
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
# Constants
FEATURE_NAMES = ["Feature 1", "Feature 2", "Feature 3", "Feature 4", "Feature 5"]
def task_func(data_matrix):

entry point

task_func

libs

• pandas
• matplotlib
• sklearn