使用 Python 将 CSV 转换为嵌套 JSON
我需要将以下 CSV 数据解析为嵌套的 JSON 字符串。请告知我如何将“ payment_mode”添加为“cashier”的嵌套值。我尝试了一些方法，例如创建另一个orderedDict并将其附加到子集列表中，但这并没有按预期工作。将不胜感激任何帮助。
CSV 数据：
Contract_no,sales_date,store_sales_amount,cashier_counter,discount_amount,before_tax_amount,tax_amount,cashier_amount,product,dine_in,take_away,mode,amount
CS,2020-04-12,18.50,C1,0,18.50,0,18.50,18.50,0,0,CASH,1068.50
预期的 JSON 格式：
{
"contract_no": "CS",
"sales_date": "2020-04-06",
"store_sales_amount": "822.17",
"cashier": [
{
"cashier_counter": "C1",
"discount_amount": "15",
"before_tax_amount": "13.15",
"tax_amount": "219.13",
"cashier_amount": "232.28",
"product":"100.12",
"dine_in":"116.02",
"take_away":"16.14",
"payment_mode": [
{
"mode": "CASH",
"amount": "112.46"
}
]
},
]
}
电流输出：
{
"contract_no": "CS",
"sales_date": "2020-04-12",
"store_sales_amount": "18.50",
"cashier": [
{
"cashier_counter": "C1",
"discount_amount": "0",
"before_tax_amount": "18.50",
"tax_amount": "0",
"cashier_amount": "18.50",
"product": "18.50",
"dine_in": "0",
"take_away": "0",
"mode": "CASH",
"cash_amount": "18.50"
}
]
}
代码
import pandas as pd
from itertools import groupby
from collections import OrderedDict
import json
#read csv into dataframe
df = pd.read_csv('sales2.csv', dtype={
#level1
"Contract_no" : str,
"sales_date" : str,
"store_sales_amount" : str,
#level2 cashier
"cashier_counter" : str,
"discount_amount" : str,
"before_tax_amount" : str,
"tax_amount" : str,
"cashier_amount" : str,
"product" : str,
"dine_in" : str,
"take_away" : str,
#level3 payment_mode
"mode" : str,
"cash_amount" : str
})
拉风的咖菲猫1回答
-
POPMUISE
你是这个意思吗？import jsoncsv = """Contract_no,sales_date,store_sales_amount,cashier_counter,discount_amount,before_tax_amount,tax_amount,cashier_amount,product,dine_in,take_away,mode,amountCS,2020-04-12,18.50,C1,0,18.50,0,18.50,18.50,0,0,CASH,1068.50"""table = [row.split(",") for row in csv.split("\n")]"""" 0 1 2 3 4 5 6 7 8 9 10 11 12|-------------|------------|--------------------|-----------------|-----------------|-------------------|------------|----------------|---------|---------|-----------|------|---------|| Contract_no | sales_date | store_sales_amount | cashier_counter | discount_amount | before_tax_amount | tax_amount | cashier_amount | product | dine_in | take_away | mode | amount ||-------------|------------|--------------------|-----------------|-----------------|-------------------|------------|----------------|---------|---------|-----------|------|---------|| CS | 2020-04-12 | 18.50 | C1 | 0 | 18.50 | 0 | 18.50 | 18.50 | 0 | 0 | CASH | 1068.50 ||-------------|------------|--------------------|-----------------|-----------------|-------------------|------------|----------------|---------|---------|-----------|------|---------|"""jsn = { "contract_no": table[1][0], "sales_date": table[1][1], "store_sales_amount": table[1][2], "cashier": [ { "cashier_counter": table[1][3], "discount_amount": table[1][4], "before_tax_amount": table[1][5], "tax_amount": table[1][6], "cashier_amount": table[1][7], "product": table[1][8], "dine_in": table[1][9], "take_away": table[1][10], "payment_mode": [ { "mode": table[1][11], "amount": table[1][12] } ] }, ]}print (json.dumps(jsn, indent=4))输出：{ "contract_no": "CS", "sales_date": "2020-04-12", "store_sales_amount": "18.50", "cashier": [ { "cashier_counter": "C1", "discount_amount": "0", "before_tax_amount": "18.50", "tax_amount": "0", "cashier_amount": "18.50", "product": "18.50", "dine_in": "0", "take_away": "0", "payment_mode": [ { "mode": "CASH", "amount": "1068.50" } ] } ]}我不知道你的工作流程。也许jsn也可以这样定义：jsn = { table[0][0]: table[1][0], table[0][1]: table[1][1], table[0][2]: table[1][2], "cashier": [ { table[0][3]: table[1][3], table[0][4]: table[1][4], table[0][5]: table[1][5], table[0][6]: table[1][6], table[0][7]: table[1][7], table[0][8]: table[1][8], table[0][9]: table[1][9], table[0][10]: table[1][10], "payment_mode": [ { table[0][11]: table[1][11], table[0][12]: table[1][12] } ] }, ]}
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