我有一个类似于this one的问题。但是,我需要部分嵌套我的JSON。目前,我的数据框如下所示:
df = pd.DataFrame({'subsidary': ['company name','company name'],
'purchase_order_number': ['PO Num', 'PO Num'],
'invoice_date': ['2018-10-15', '2018-10-15'],
'vendor_invoice_number': ['777','777'],
'vendor_sku': ['SKU888', 'SKU888'],
'quantity': ['10', '20'],
'rate': ['12.00', '11.00'],
'amount': ['120.00', '220.00'],
'freight': ['5.00', '5.00'],
'taxes': ['0.00', '0.00']})
使用上面的链接和下面的代码:
j = (df.groupby(['subsidary','purchase_order_number','invoice_date','vendor_invoice_number'], as_index=False)
.apply(lambda x: x[['vendor_sku','quantity','rate','amount']].to_dict('r'))
.reset_index()
.rename(columns={0:'item_charges'})
.to_json(orient='records'))
print(json.dumps(json.loads(j), indent=2, sort_keys=False))
我能够使它看起来像这样:
[
{
"subsidary": "company name",
"purchase_order_number": "PO Num",
"invoice_date": "2018-10-15",
"vendor_invoice_number": "777",
"item_charges": [
{
"vendor_sku": "SKU888",
"quantity": "10",
"rate": "12.00",
"amount": "120.00"
},
{
"vendor_sku": "SKU888",
"quantity": "20",
"rate": "11.00",
"amount": "220.00"
}
]
}
]
但是,我希望它看起来像这样:
[
{
"subsidary": "Natural Partners",
"purchase_order_number": "AZ003387-PO",
"invoice_date": "2018-10-15",
"vendor_invoice_number": "76947",
"item_charges": [
{
"vendor_sku": "SUP002",
"quantity": "12.00",
"rate": "14.50",
"amount": "174.00"
},
{
"vendor_sku": "SUP004",
"quantity": "3.00",
"rate": "8.75",
"amount": "26.25"
}
],
"invoice_charges":
{
"freight": '5.00',
"taxes": '0.00',
}
}
]
我是否可以在python中执行此操作?
谢谢。
答案 0 :(得分:1)
您可以通过在处理下一个嵌套之前存储每个嵌套来做到这一点。
df = pd.DataFrame({'subsidary': ['company name','company name'],
'purchase_order_number': ['PO Num', 'PO Num'],
'invoice_date': ['2018-10-15', '2018-10-15'],
'vendor_invoice_number': ['777','777'],
'vendor_sku': ['SKU888', 'SKU888'],
'quantity': ['10', '20'],
'rate': ['12.00', '11.00'],
'amount': ['120.00', '220.00'],
'freight': ['5.00', '5.00'],
'taxes': ['0.00', '0.00']})
# Your original procedure
j = df.groupby(
['subsidary','purchase_order_number','invoice_date',
'vendor_invoice_number', "freight", "taxes"],
as_index=False).apply(lambda x: x[['vendor_sku','quantity','rate','amount']].to_dict('r')
).reset_index().rename(columns={0:'item_charges'})
# Store the item_charges and do it again
item_charges = j["item_charges"]
j=j.groupby(['subsidary','purchase_order_number','invoice_date',
'vendor_invoice_number',"freight", "taxes"], as_index=False
).apply(lambda x: x[["freight", "taxes"]].to_dict('r')
).reset_index().rename(columns={0:'invoice_charges'})
# Add back the stored item_charges
j["item_charges"] = item_charges
j = j.to_json(orient='records')
print(json.dumps(json.loads(j), indent=2, sort_keys=False))
我应该说,我对这种方法并不感到兴奋,也无法想象它是高效的,但这是我能想到的。它可以正常工作-输出如下:
[
{
"subsidary": "company name",
"purchase_order_number": "PO Num",
"invoice_date": "2018-10-15",
"vendor_invoice_number": "777",
"freight": "5.00",
"taxes": "0.00",
"invoice_charges": [
{
"freight": "5.00",
"taxes": "0.00"
}
],
"item_charges": [
{
"vendor_sku": "SKU888",
"quantity": "10",
"rate": "12.00",
"amount": "120.00"
},
{
"vendor_sku": "SKU888",
"quantity": "20",
"rate": "11.00",
"amount": "220.00"
}
]
}
]