Collect data from the Betfair API using the betfairlightweight module and create a DataFrame including existing values in each Series

Question

My code uses a Series like the one below to create a final DataFrame adding other values that will be collected after access Betfair API.
Example for row_df:

country                                  BR
competition    Brazilian Paranaense Matches
match_id                           32055988
match_name           Londrina v Operario PR
market_id                       1.209139372
market_name            First Half Goals 0.5
Home                        Under 0.5 Goals
Home_id                             5851482
Away                         Over 0.5 Goals
Away_id                             5851483
Name: 0, dtype: object

After adding the values that I want for each Series, I add them in the DataFrame and at the end, I add the columns that already existed in row_df because they will be useful and the same for all the created rows of the DataFrame:

def total_matched(trading,row_df):
    price_filter = betfairlightweight.filters.price_projection(
        price_data=['EX_BEST_OFFERS','EX_TRADED']
    )
    market_books = trading.betting.list_market_book(
        market_ids=[row_df['market_id']],
        price_projection=price_filter
    )

    runners = market_books[0].runners
    status = market_books[0].status

    df_runners = pd.DataFrame()
    if status == 'OPEN':
        for runner in runners:
            id = runner.selection_id
            name = row_df['Home'] if id == row_df['Home_id'] else row_df['Away'] if id == row_df['Away_id'] else '-'
            tm = runner.total_matched if runner.total_matched is not None else np.nan
            lpt = runner.last_price_traded if runner.last_price_traded is not None else np.nan
            bbp = runner.ex.available_to_back[0].price if len(runner.ex.available_to_back) > 0 and runner.ex.available_to_back[0].price is not None else np.nan
            blp = runner.ex.available_to_lay[0].price if len(runner.ex.available_to_lay) > 0 and runner.ex.available_to_lay[0].price is not None else np.nan
            df_runners = pd.concat([df_runners, pd.DataFrame({
                # Basics
                'runner_id': id,
                'runner_name': name,
                'status': status,
                'total_matched': tm,
                # Minute Traded
                'historic_minute_traded_1':np.NaN,
                'historic_minute_traded_2':np.NaN,
                'historic_minute_traded_3':np.NaN,
                'historic_minute_traded_4':np.NaN,
                'historic_minute_traded_5':np.NaN,
                'minute_traded':np.NaN,
                # Last Price Traded
                'historic_last_price_traded_1':np.NaN,
                'historic_last_price_traded_2':np.NaN,
                'historic_last_price_traded_3':np.NaN,
                'historic_last_price_traded_4':np.NaN,
                'last_price_traded': lpt,
                'last_price_traded_0_decimals':np.trunc(1 * lpt) / 1,
                'last_price_traded_1_decimals':np.trunc(10 * lpt) / 10,
                'last_price_traded_2_decimals':np.trunc(100 * lpt) / 100,
                # Best Back Price
                'historic_best_back_price_1':np.NaN,
                'historic_best_back_price_2':np.NaN,
                'historic_best_back_price_3':np.NaN,
                'historic_best_back_price_4':np.NaN,
                'best_back_price':bbp,
                'best_back_price_0_decimals':np.trunc(1 * bbp) / 1,
                'best_back_price_1_decimals':np.trunc(10 * bbp) / 10,
                'best_back_price_2_decimals':np.trunc(100 * bbp) / 100,
                # Best Lay Price
                'historic_best_lay_price_1':np.NaN,
                'historic_best_lay_price_2':np.NaN,
                'historic_best_lay_price_3':np.NaN,
                'historic_best_lay_price_4':np.NaN,
                'best_lay_price':blp,
                'best_lay_price_0_decimals':np.trunc(1 * blp) / 1,
                'best_lay_price_1_decimals':np.trunc(10 * blp) / 10,
                'best_lay_price_2_decimals':np.trunc(100 * blp) / 100,
                # Results
                'result':'',
                'result_hour_recorded':''
            },index=[0])], ignore_index=True)

        cols = list(row_df.keys()) + list(df_runners.columns)
        df_runners = df_runners.assign(**row_df)
        df_runners = df_runners.reindex(columns=cols)

    return df_runners

I would like help to improve the model and make it faster and more efficient.

The sequence of the columns I am adding are important for further use of the DataFrame.