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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _create_significance_table(self,data):
 """ Create a table containing p-values for significance tests. Add features of the distributions and the p-values to the dataframe. Parameters data : pandas DataFrame The input dataset. Returns df : pandas DataFrame A table containing the p-values, test name, etc. """

        # list features of the variable e.g. matched, paired, n_expected
        df=pd.DataFrame(index=self._continuous+self._categorical,
            columns=['continuous','nonnormal','min_observed','pval','ptest'])

        df.index.rename('variable', inplace=True)
        df['continuous'] = np.where(df.index.isin(self._continuous),True,False)
        df['nonnormal'] = np.where(df.index.isin(self._nonnormal),True,False)

        # list values for each variable, grouped by groupby levels
        for v in df.index:
            is_continuous = df.loc[v]['continuous']
            is_categorical = ~df.loc[v]['continuous']
            is_normal = ~df.loc[v]['nonnormal']

            # if continuous, group data into list of lists
            if is_continuous:
                catlevels = None
                grouped_data = []
                for s in self._groupbylvls:
                    lvl_data = data.loc[data[self._groupby]==s, v]
                    # coerce to numeric and drop non-numeric data
                    lvl_data = lvl_data.apply(pd.to_numeric, errors='coerce').dropna()
                    # append to overall group data
                    grouped_data.append(lvl_data.values)
                min_observed = len(min(grouped_data,key=len))
            # if categorical, create contingency table
            elif is_categorical:
                catlevels = sorted(data[v].astype('category').cat.categories)
                grouped_data = pd.crosstab(data[self._groupby].rename('_groupby_var_'),data[v])
                min_observed = grouped_data.sum(axis=1).min()

            # minimum number of observations across all levels
            df.loc[v,'min_observed'] = min_observed

            # compute pvalues
            df.loc[v,'pval'],df.loc[v,'ptest'] = self._p_test(v,
                grouped_data,is_continuous,is_categorical,
                is_normal,min_observed,catlevels)

        return df