将嵌套的 Json 文件展平为 Pandas 数据框
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【中文标题】将嵌套的 Json 文件展平为 Pandas 数据框【英文标题】：Flattening nested Json file into pandas dataframe 【发布时间】：2021-05-14 08:32:42 【问题描述】：我有这个 json 文件
"OrderMaster":
"Order":
"item": [
"row_id": "1-2LDPVI0",
"sequence_id": "3851101",
"end_date": "",
"name": "TV-Discount",
"orderable": "Y",
"period": "",
"period_uom": "",
"phone_number_flag": "N",
"price_type": "Recurring",
"product_category": "mobilepackage",
"product_sub_category": "Discount",
"product_type_code": "Product",
"type": "PhoneOrder",
"vendor_part_number": "",
"created_date": "2018-02-16 09:09:24",
"created_by": "id123",
"last_updated_date": "2020-09-14 09:39:24",
"last_updated_by": "id123",
"ts_event_notification_time": "2020-09-14 09:40:69",
"OrderItems":
"item": [
"original_list_price": "0",
"order_list_id": "1-4ABU",
"order_list_name": "SEK Pricelist",
"product_id": "1-2LDPUKX",
"start_date": "2018-02-17 00:00:00"
,
"original_list_price": "45",
"order_list_id": "1-4AFU",
"order_list_name": "SEK Pricelist",
"product_id": "1-2LGSDFUKX",
"start_date": "2018-02-18 00:04:20"
]
,
"row_id": "1-2LDPVI0",
"sequence_id": "3851101",
"end_date": "",
"name": "TV-Discount",
"orderable": "Y",
"period": "",
"period_uom": "",
"phone_number_flag": "N",
"price_type": "Recurring",
"product_category": "mobilepackage",
"product_sub_category": "Discount",
"product_type_code": "Product",
"type": "PhoneOrder",
"vendor_part_number": "",
"created_date": "2018-02-16 09:19:24",
"created_by": "id123",
"last_updated_date": "2020-09-15 09:39:24",
"last_updated_by": "id123",
"ts_event_notification_time": "2020-09-14 09:40:28",
"OrderItems":
"item": [
"original_list_price": "42",
"order_list_id": "1-4ABU",
"order_list_name": "SEK Pricelist",
"product_id": "1-2LDPUKX",
"start_date": "2018-02-19 00:00:00"
,
"original_list_price": "42",
"order_list_id": "1-4ASU",
"order_list_name": "SEK Pricelist",
"product_id": "1-2LDDAKX",
"start_date": "2018-02-12 00:00:00"
,
"original_list_price": "43",
"order_list_id": "1-4FDBU",
"order_list_name": "SEK Pricelist",
"product_id": "1-2LDFSDFKX",
"start_date": "2018-02-11 00:00:00"
]
]
这就是我想要实现的目标：
到目前为止，我已经设法找到了这个point 但是我对最后一个嵌套列“OrderItem”列有问题。我设法extract 它但很难弄清楚如何将它们连接在一起，就像在目标结果中一样。
【问题讨论】：
【参考方案1】：我设法通过使用带有正确参数集的 json_normalise 解决了这个问题
with open(file_path) as f:
data = json.load(f)
# Define feature list for dataframe
features = [
"row_id",
"sequence_id",
"end_date",
"name",
"orderable",
"period",
"period_uom",
"phone_number_flag",
"price_type",
"product_category",
"product_sub_category",
"product_type_code",
"type",
"vendor_part_number",
"created_date",
"created_by",
"last_updated_date",
"last_updated_by",
"ts_event_notification_time"
]
# Create dataframe using json_normalize pandas function with necessary parameters
df = pd.json_normalize(data['OrderMaster']['Order']['item'],['OrderItems', 'item'], features)
结果是每个项目的完整行数据：
【讨论】：
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