如何将熊猫数据框中的嵌套逗号分隔列转换为Python中的特定格式

Question

我有数据框(df)如下图

输入

Df

ID  linkedShipments
12      [{'SID': 'GBDXY551', 'Code': 'GBDXY55', 'Num': '2021121'}, {'SID': 'GBDXY551', 'Code': 'GBDXY55', 'Num': '20211215'}]
32      [{'SID': 'CHSGI422', 'Code': 'CHSGI421', 'Num': '4024421'}, {'SID': 'GBDXY551', 'Code': 'GBDXY55', 'Num': '20211222'}]

预期输出

ID  SID         Code     Num
12  GBDXY551    GBDXY55  2021121
12  GBDXY551    GBDXY55  20211215
32  CHSGI422    CHSGI421 4024421
32  GBDXY551    GBDXY55  20211222

如何在 pandas 中做到这一点？

Answer 1

最简单的方法是：

1. json.loads 每一行linkedShipments
2. 遍历linkedShipments中的所有元素
3. 将 ID 和 linkedShipments 的每个元素组合为行字典
4. 附加到新数据

下面是python代码

import pandas as pd
import json

#The simulation of data
data = {"ID": [12, 24],
    "linkedShipments": ["[{'SID': 'GBDXY551', 'Code': 'GBDXY55', 'Num': '2021121'}, {'SID': 'GBDXY551', 'Code': 'GBDXY55', 'Num': '20211215'}]", 
    "[{'SID': 'CHSGI422', 'Code': 'CHSGI421', 'Num': '4024421'}, {'SID': 'GBDXY551', 'Code': 'GBDXY55', 'Num': '20211222'}]"]
   }
#load as dataframe
df = pd.DataFrame(data)

new_data = {} #define new data
#treverse all rows in current data
for index, row in df.iterrows():
    #json only accept double quotes, so convert singal quotes to double quotes
    shipment_dict_list = json.loads(row['linkedShipments'].replace("\'", "\"")) 
    for shipment_dict in shipment_dict_list:
        new_data.setdefault("ID",[]).append(row['ID'])
        for key in shipment_dict:
            new_data.setdefault(key,[]).append(shipment_dict[key])
print(pd.DataFrame(new_data))

结果如下：

   ID       SID      Code       Num
0  12  GBDXY551   GBDXY55   2021121
1  12  GBDXY551   GBDXY55  20211215
2  24  CHSGI422  CHSGI421   4024421
3  24  GBDXY551   GBDXY55  20211222