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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 add_row(self, label, row_data, columns=""):
 """ Add a row with data. If any new keys are present in row_data dictionary, that column will be added to the dataframe. This is done inplace """

        # use provided column order, making sure you don't lose any values
        # from self.df.columns
        if len(columns):
            if sorted(self.df.columns) == sorted(columns):
                self.df.columns = columns
            else:
                new_columns = []
                new_columns.extend(columns)
                for col in self.df.columns:
                    if col not in new_columns:
                        new_columns.append(col)
        # makes sure all columns have data or None
        if sorted(row_data.keys()) != sorted(self.df.columns):
            # add any new column names
            for key in row_data:
                if key not in self.df.columns:
                    self.df[key] = None
            # add missing column names into row_data
            for col_label in self.df.columns:
                if col_label not in list(row_data.keys()):
                    row_data[col_label] = None

        # (make sure you are working with strings)
        self.df.index = self.df.index.astype(str)
        label = str(label)

        # create a new row with suffix "new"
        # (this ensures that you get a unique, new row,
        #  instead of adding on to an existing row with the same label)
        self.df.loc[label + "new"] = pd.Series(row_data)
        # rename it to be correct
        self.df.rename(index={label + "new": label}, inplace=True)
        # use next line to sort index inplace
        #self.df.sort_index(inplace=True)
        return self.df