BigCodeBench / BigCodeBench v0.1.0_hf 88128b25-7465-56ea-9965-f84e628b353f

complete prompt

from sklearn.preprocessing import StandardScaler
import seaborn as sns
import matplotlib.pyplot as plt


def task_func(df):
    """
    Standardize numeric columns in a DataFrame and return the heatmap of the correlation matrix. Missing values are replaced by the column's average.

    Parameters:
    - df (pandas.DataFrame): The pandas DataFrame to be standardized.

    Returns:
    - DataFrame: The pandas DataFrame after standardization.
    - Axes: A heatmap of the correlation matrix.

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

    Example:
    >>> import pandas as pd
    >>> import numpy as np
    >>> df = pd.DataFrame([[1,2,3],[4,5,6],[7.0,np.nan,9.0]], columns=["c1","c2","c3"])
    >>> standardized_df, heatmap = task_func(df)
    >>> print(standardized_df)
             c1        c2        c3
    0 -1.224745 -1.224745 -1.224745
    1  0.000000  1.224745  0.000000
    2  1.224745  0.000000  1.224745
    """

instruct prompt

Standardize numeric columns in a DataFrame and return the heatmap of the correlation matrix. Missing values are replaced by the column's average.
The function should output with:
    DataFrame: The pandas DataFrame after standardization.
    Axes: A heatmap of the correlation matrix.
You should write self-contained code starting with:

Code

from sklearn.preprocessing import StandardScaler
import seaborn as sns
import matplotlib.pyplot as plt
def task_func(df):

code prompt

Code

from sklearn.preprocessing import StandardScaler
import seaborn as sns
import matplotlib.pyplot as plt
def task_func(df):

entry point

task_func

libs

• sklearn
• matplotlib
• seaborn