[Fixing Test-Cases] Pandas/Numpy use different stdev estimators (mean_std_column)

[ian-coccimiglio]

Pandas and numpy use different approximations for standard deviation. As such, this explains why this test gets such a low score. Related to #76 and one of the not-improving tests in #118

I think we should either specify the desired estimator in the prompt (unbiased or biased) or make the test flexible enough to accept either answer (the majority of the models use the builtin pandas stdev, but we only accept the numpy estimator).

This is our reference test function

def mean_std_column(dataframe, column:str):
    """
    Computes the mean average and standard deviation of a specified column 
    in a given dataframe and returns these two values.
    """
    import numpy as np
    data = dataframe[column]
    return np.mean(data), np.std(data)

And this is what most models provide:

import pandas as pd

def mean_std_column(dataframe, column:str):
    """
    Computes the mean average and standard deviation of a specified column 
    in a given dataframe and returns these two values.
    """
    mean_value = dataframe[column].mean()
    std_value = dataframe[column].std()
    return (mean_value, std_value)

[haesleinhuepf]

make the test flexible enough to accept either answer (the majority of the models use the builtin pandas stdev, but we only accept the numpy estimator).

I vote for flexibility. Both solutions should be detected as correct.

[ian-coccimiglio]

Done! I put in a PR for this one.