def aggregate_per_customer_over_time_period(df
                  , end_date = '2018-03-02'
                  , nr_days_back = 30
                  , cust_col_name = 'CustomerNumber'
                  , date_col_name='Date'
                  , variable_col_name = 'DisputeExists'
                  , agg_func = np.mean
                 ):
    from datetime import datetime, timedelta
    df[date_col_name] = df[date_col_name].astype(str)

    start_date = str(datetime.strptime(end_date,"%Y-%m-%d")-timedelta(days=nr_days_back))[:10]

    df = df[(df[date_col_name]<end_date) & (df[date_col_name]>=start_date)]

    df = df.groupby(cust_col_name).agg(agg_func)[variable_col_name].reset_index()

    df['AnchorDate'] = end_date

    return df