Python-链接2列

我想创建一个数据框架，将两列链接在一起（客户ID与客户下的每个订单ID）。行索引+1与客户ID相关。有方法做到这一点吗？

数据：invoice_df

Order Id,Date,Meal Id,Company Id,Date of Meal,Participants,Meal Price,Type of Meal
839FKFW2LLX4LMBB,27-05-2016,INBUX904GIHI8YBD,LJKS5NK6788CYMUU,2016-05-31 07:00:00+02:00,['David Bishop'],469,Breakfast
97OX39BGVMHODLJM,27-09-2018,J0MMOOPP709DIDIE,LJKS5NK6788CYMUU,2018-10-01 20:00:00+02:00,['David Bishop'],22,Dinner
041ORQM5OIHTIU6L,24-08-2014,E4UJLQNCI16UX5CS,LJKS5NK6788CYMUU,2014-08-23 14:00:00+02:00,['Karen Stansell'],314,Lunch
YT796QI18WNGZ7ZJ,12-04-2014,C9SDFHF7553BE247,LJKS5NK6788CYMUU,2014-04-07 21:00:00+02:00,['Addie Patino'],438,Dinner
6YLROQT27B6HRF4E,28-07-2015,48EQXS6IHYNZDDZ5,LJKS5NK6788CYMUU,2015-07-27 14:00:00+02:00,['Addie Patino' 'Susan Guerrero'],690,Lunch
AT0R4DFYYAFOC88Q,21-07-2014,W48JPR1UYWJ18NC6,LJKS5NK6788CYMUU,2014-07-17 20:00:00+02:00,['David Bishop' 'Susan Guerrero' 'Karen Stansell'],181,Dinner
2DDN2LHS7G85GKPQ,29-04-2014,1MKLAKBOE3SP7YUL,LJKS5NK6788CYMUU,2014-04-30 21:00:00+02:00,['Susan Guerrero' 'David Bishop'],14,Dinner
FM608JK1N01BPUQN,08-05-2014,E8WJZ1FOSKZD2MJN,36MFTZOYMTAJP1RK,2014-05-07 09:00:00+02:00,['Amanda Knowles' 'Cheryl Feaster' 'Ginger Hoagland' 'Michael White'],320,Breakfast
CK331XXNIBQT81QL,23-05-2015,CTZSFFKQTY7SBZ4J,36MFTZOYMTAJP1RK,2015-05-18 13:00:00+02:00,['Cheryl Feaster' 'Amanda Knowles' 'Ginger Hoagland'],697,Lunch
FESGKOQN2OZZWXY3,10-01-2016,US0NQYNNHS1SQJ4S,36MFTZOYMTAJP1RK,2016-01-14 22:00:00+01:00,['Glenn Gould' 'Amanda Knowles' 'Ginger Hoagland' 'Michael White'],451,Dinner
YITOTLOF0MWZ0VYX,03-10-2016,RGYX8772307H78ON,36MFTZOYMTAJP1RK,2016-10-01 22:00:00+02:00,['Ginger Hoagland' 'Amanda Knowles' 'Michael White'],263,Dinner
8RIGCF74GUEQHQEE,23-07-2018,5XK0KTFTD6OAP9ZP,36MFTZOYMTAJP1RK,2018-07-27 08:00:00+02:00,['Amanda Knowles'],210,Breakfast
TH60C9D8TPYS7DGG,15-12-2016,KDSMP2VJ22HNEPYF,36MFTZOYMTAJP1RK,2016-12-13 08:00:00+01:00,['Cheryl Feaster' 'Bret Adams' 'Ginger Hoagland'],755,Breakfast
W1Y086SRAVUZU1AL,17-09-2017,8IUOYVS031QPROUG,36MFTZOYMTAJP1RK,2017-09-14 13:00:00+02:00,['Bret Adams'],469,Lunch
WKB58Q8BHLOFQAB5,31-08-2016,E2K2TQUMENXSI9RP,36MFTZOYMTAJP1RK,2016-09-03 14:00:00+02:00,['Michael White' 'Ginger Hoagland' 'Bret Adams'],502,Lunch
N8DOG58MW238BHA9,25-12-2018,KFR2TAYXZSVCHAA2,36MFTZOYMTAJP1RK,2018-12-20 12:00:00+01:00,['Ginger Hoagland' 'Cheryl Feaster' 'Glenn Gould' 'Bret Adams'],829,Lunch
DPDV9UGF0SUCYTGW,25-05-2017,6YV61SH7W9ECUZP0,36MFTZOYMTAJP1RK,2017-05-24 22:00:00+02:00,['Michael White'],708,Dinner
KNF3E3QTOQ22J269,20-06-2018,737T2U7604ABDFDF,36MFTZOYMTAJP1RK,2018-06-15 07:00:00+02:00,['Glenn Gould' 'Cheryl Feaster' 'Ginger Hoagland' 'Amanda Knowles'],475,Breakfast
LEED1HY47M8BR5VL,22-10-2017,I22P10IQQD06MO45,36MFTZOYMTAJP1RK,2017-10-22 14:00:00+02:00,['Glenn Gould'],27,Lunch
LSJPNJQLDTIRNWAL,27-01-2017,247IIVNN6CXGWINB,36MFTZOYMTAJP1RK,2017-01-23 13:00:00+01:00,['Amanda Knowles' 'Bret Adams'],672,Lunch
6UX5RMHJ1GK1F9YQ,24-08-2014,LL4AOPXDM8V5KP5S,H3JRC7XX7WJAD4ZO,2014-08-27 12:00:00+02:00,['Anthony Emerson' 'Irvin Gentry' 'Melba Inlow'],552,Lunch
5SYB15QEFWD1E4Q4,09-07-2017,KZI0VRU30GLSDYHA,H3JRC7XX7WJAD4ZO,2017-07-13 08:00:00+02:00,"['Anthony Emerson' 'Emma Steitz' 'Melba Inlow' 'Irvin Gentry'
 'Kelly Killebrew']",191,Breakfast
W5S8VZ61WJONS4EE,25-03-2017,XPSPBQF1YLIG26N1,H3JRC7XX7WJAD4ZO,2017-03-25 07:00:00+01:00,['Irvin Gentry' 'Kelly Killebrew'],471,Breakfast
795SVIJKO8KS3ZEL,05-01-2015,HHTLB8M9U0TGC7Z4,H3JRC7XX7WJAD4ZO,2015-01-06 22:00:00+01:00,['Emma Steitz'],588,Dinner
8070KEFYSSPWPCD0,05-08-2014,VZ2OL0LREO8V9RKF,H3JRC7XX7WJAD4ZO,2014-08-09 12:00:00+02:00,['Lewis Eyre'],98,Lunch
RUQOHROBGBOSNUO4,10-06-2016,R3LFUK1WFDODC1YF,H3JRC7XX7WJAD4ZO,2016-06-09 08:00:00+02:00,['Anthony Emerson' 'Kelly Killebrew' 'Lewis Eyre'],516,Breakfast
6P91QRADC2O9WOVT,25-09-2016,L2F2HEGB6Q141080,H3JRC7XX7WJAD4ZO,2016-09-26 07:00:00+02:00,"['Kelly Killebrew' 'Lewis Eyre' 'Irvin Gentry' 'Emma Steitz'
 'Anthony Emerson']",664,Breakfast

代码：

# Function to convert string ['name' 'name2'] to list ['name', 'name2']
# Returns a list of participant names
def string_to_list(participant_string): return re.findall(r"'(.*?)'", participant_string)
invoice_df["Participants"] = invoice_df["Participants"].apply(string_to_list)

# Obtain an array of all unique customer names
customers = invoice_df["Participants"].explode().unique()

# Create new customer dataframe
customers_df = pd.DataFrame(customers, columns = ["CustomerName"])

# Add customer id
customers_df["customer_id"] = customers_df.index + 1

# Create a first_name and last_name column
customers_df["first_name"] = customers_df["CustomerName"].apply(lambda x: x.split(" "[0])

# Splice the list 1: in the event the person has multiple last names
customers_df["last_name"] = customers_df["CustomerName"].apply(lambda x: x.split(" ")[1])