How to use the pingouin.parametric.ttest function in pingouin

TOST    0.5    5  0.954854

    3. One sample TOST

    >>> pg.tost(a, y=0, bound=4)
          bound  dof      pval
    TOST      4    5  0.825967
    """
    x = np.asarray(x)
    y = np.asarray(y)
    assert isinstance(bound, (int, float)), 'bound must be int or float.'

    # T-tests
    df_a = ttest(x + bound, y, paired=paired, correction=correction,
                 tail='greater')
    df_b = ttest(x - bound, y, paired=paired, correction=correction,
                 tail='less')
    pval = max(df_a.at['T-test', 'p-val'], df_b.at['T-test', 'p-val'])

    # Create output dataframe
    stats = {'bound': bound, 'dof': df_a.at['T-test', 'dof'], 'pval': pval}
    return pd.DataFrame.from_records(stats, index=['TOST'])

>>> pg.tost(a, b, bound=0.5, paired=True)
          bound  dof      pval
    TOST    0.5    5  0.954854

    3. One sample TOST

    >>> pg.tost(a, y=0, bound=4)
          bound  dof      pval
    TOST      4    5  0.825967
    """
    x = np.asarray(x)
    y = np.asarray(y)
    assert isinstance(bound, (int, float)), 'bound must be int or float.'

    # T-tests
    df_a = ttest(x + bound, y, paired=paired, correction=correction,
                 tail='greater')
    df_b = ttest(x - bound, y, paired=paired, correction=correction,
                 tail='less')
    pval = max(df_a.at['T-test', 'p-val'], df_b.at['T-test', 'p-val'])

    # Create output dataframe
    stats = {'bound': bound, 'dof': df_a.at['T-test', 'dof'], 'pval': pval}
    return pd.DataFrame.from_records(stats, index=['TOST'])

stats[cols_bool] = stats[cols_bool].astype(bool)

        # Fill str columns
        stats.loc[:, 'A'] = A
        stats.loc[:, 'B'] = B
        stats.loc[:, 'Contrast'] = col
        stats.loc[:, 'Tail'] = tail
        stats.loc[:, 'Paired'] = paired

        for i in range(stats.shape[0]):
            col1, col2 = stats.at[i, 'A'], stats.at[i, 'B']
            x = grp_col.get_group(col1).to_numpy(dtype=np.float64)
            y = grp_col.get_group(col2).to_numpy(dtype=np.float64)
            if parametric:
                stat_name = 'T'
                df_ttest = ttest(x, y, paired=paired, tail=tail)
                stats.at[i, 'BF10'] = df_ttest.at['T-test', 'BF10']
                stats.at[i, 'dof'] = df_ttest.at['T-test', 'dof']
            else:
                if paired:
                    stat_name = 'W-val'
                    df_ttest = wilcoxon(x, y, tail=tail)
                else:
                    stat_name = 'U-val'
                    df_ttest = mwu(x, y, tail=tail)

            # Compute Hedges / Cohen
            ef = np.round(compute_effsize(x=x, y=y, eftype=effsize,
                                          paired=paired), 3)

            if return_desc:
                stats.at[i, 'mean(A)'] = np.round(np.nanmean(x), 3)