通过自定义查找所有以前的事件ID的最佳方法

Question

我正在处理这个看起来相当简单的问题。数据如下所示：

例如，customer ID的当前事件ID是abc。现在，我需要以列表格式查找所有客户的所有eventId，以便记录第一个事件ID，然后记录下一个直到最新的事件ID

我为一个客户使用的方法如下：

import pandas as pd
import numpy as np
data = pd.read_csv('test.csv')
data.to_dict()
{'customerid': {0: 233, 1: 250, 2: 233, 3: 250, 4: 233},
 'eventid': {0: 'abc', 1: 'bcd', 2: 'edc', 3: 'abl', 4: 'cdl'},
 'date': {0: '2019-12-10',
  1: '2019-12-08',
  2: '2008-12-10',
  3: '2019-12-01',
  4: '2001-12-10'},
 'previouseventid': {0: 'edc', 1: 'abl', 2: 'cdl', 3: np.nan, 4: np.nan}}

    customerid  eventid date    previouseventid
0   233 abc 2019-12-10  edc
1   250 bcd 2019-12-08  abl
2   233 edc 2008-12-10  cdl
3   250 abl 2019-12-01  
4   233 cdl 2001-12-10  

temp = [cust_233['eventid'][0]]

for i in range(len(cust_233['previouseventid'])-1):
    if pd.isna(cust_233['previouseventid'][i]) == False:
        # print(cust_233['previouseventid'][i])
        temp.append(cust_233['previouseventid'][i])
    else:
        # print('now exiting')
        break

我觉得我的方法有点笨重，有很多代码。如何有效地为所有客户解决问题？你知道吗

更新：

我需要的输出是列表。 客户233的预期输出是列表['cdl'，'edc'，'abc']，客户250的预期输出是['abl'，'bcd']

Answer 1

Groupby然后转移应该起作用：

# First, make sure your data is sorted from oldest to newest
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)

# Get previous event through groupby operation
df['prev_id'] = df.groupby('customerid')['eventid'].shift(1)

如果您想要每个客户的列表：

# create a dictionary with stored values – keys are customer id
prev_events_dict = df.groupby('customerid')['eventid'].apply(list).to_dict()
# map dict to dataframe
df['list_of_prev_id'] = df['customerid'].map(prev_events_dict)