Python csv到嵌套JSON？

我正在尝试将平面结构的CSV转换为嵌套的JSON结构。CSV由SQL生成，SQL为每个主id创建多行。CSV的结构如下：

PrimaryId，FirstName，LastName，City，CarName，DogName
100，约翰，史密斯，纽约，丰田，斯派克
100，约翰，史密斯，纽约，宝马，斯派克
100，约翰，史密斯，纽约，丰田，拉斯蒂
100，约翰，史密斯，纽约，宝马，拉斯蒂
101，本，斯旺，悉尼，大众，巴迪
101，本，斯旺，悉尼，福特，伙计
101，本，斯旺，悉尼，奥迪，巴迪
101，本，斯旺，悉尼，大众，马克斯
101号，本，斯旺，悉尼，福特，马克斯
101，本，斯旺，悉尼，奥迪，马克斯
102，朱莉娅，布朗，伦敦，米尼，露西

所需的JSON输出是：

{
    "data": [
        {
            "City": "NewYork", 
            "FirstName": "John", 
            "PrimaryId": 100, 
            "LastName": "Smith", 
            "CarName": [
                "Toyota", 
                "BMW"
            ], 
            "DogName": [
                "Spike", 
                "Rusty"
            ]
        }, 
        {
            "City": "Sydney", 
            "FirstName": "Ben", 
            "PrimaryId": 101, 
            "LastName": "Swan", 
            "CarName": [
                "Volkswagen", 
                "Ford", 
                "Audi"
            ], 
            "DogName": [
                "Buddy", 
                "Max"
            ]
        }, 
        {
            "City": "London", 
            "FirstName": "Julia", 
            "PrimaryId": 102, 
            "LastName": "Brown", 
            "CarName": [
                "Mini"
            ], 
            "DogName": [
                "Lucy"
            ]
        }
    ]
}

---

两者都有帮助，但我还没有创建正确的结构。

您转换为有效csv的数据保存在

数据中。csv

：

PrimaryId,FirstName,LastName,City,CarName,DogName
100,John,Smith,NewYork,Toyota,Spike
100,John,Smith,NewYork,BMW,Spike
100,John,Smith,NewYork,Toyota,Rusty
100,John,Smith,NewYork,BMW,Rusty
101,Ben,Swan,Sydney,Volkswagen,Buddy
101,Ben,Swan,Sydney,Ford,Buddy
101,Ben,Swan,Sydney,Audi,Buddy
101,Ben,Swan,Sydney,Volkswagen,Max
101,Ben,Swan,Sydney,Ford,Max
101,Ben,Swan,Sydney,Audi,Max
102,Julia,Brown,London,Mini,Lucy

使用熊猫完成繁重的工作，并假设此csv文件有效，这是实现您想要的一种方式：

import json
import pandas as pd

df = pd.read_csv('data.csv')

def get_nested_rec(key, grp):
    rec = {}
    rec['PrimaryId'] = key[0]
    rec['FirstName'] = key[1]
    rec['LastName'] = key[2]
    rec['City'] = key[3]

    for field in ['CarName','DogName']:
        rec[field] = list(grp[field].unique())

    return rec

records = []
for key, grp in df.groupby(['PrimaryId','FirstName','LastName','City']):
    rec = get_nested_rec(key, grp)
    records.append(rec)

records = dict(data = records)

print(json.dumps(records, indent=4))

结果是：

{
    "data": [
        {
            "City": "NewYork", 
            "FirstName": "John", 
            "PrimaryId": 100, 
            "LastName": "Smith", 
            "CarName": [
                "Toyota", 
                "BMW"
            ], 
            "DogName": [
                "Spike", 
                "Rusty"
            ]
        }, 
        {
            "City": "Sydney", 
            "FirstName": "Ben", 
            "PrimaryId": 101, 
            "LastName": "Swan", 
            "CarName": [
                "Volkswagen", 
                "Ford", 
                "Audi"
            ], 
            "DogName": [
                "Buddy", 
                "Max"
            ]
        }, 
        {
            "City": "London", 
            "FirstName": "Julia", 
            "PrimaryId": 102, 
            "LastName": "Brown", 
            "CarName": [
                "Mini"
            ], 
            "DogName": [
                "Lucy"
            ]
        }
    ]
}

---

转换为有效csv的数据保存在

data.csv

：

PrimaryId,FirstName,LastName,City,CarName,DogName
100,John,Smith,NewYork,Toyota,Spike
100,John,Smith,NewYork,BMW,Spike
100,John,Smith,NewYork,Toyota,Rusty
100,John,Smith,NewYork,BMW,Rusty
101,Ben,Swan,Sydney,Volkswagen,Buddy
101,Ben,Swan,Sydney,Ford,Buddy
101,Ben,Swan,Sydney,Audi,Buddy
101,Ben,Swan,Sydney,Volkswagen,Max
101,Ben,Swan,Sydney,Ford,Max
101,Ben,Swan,Sydney,Audi,Max
102,Julia,Brown,London,Mini,Lucy

使用熊猫完成繁重的工作，并假设此csv文件有效，这是实现您想要的一种方式：

import json
import pandas as pd

df = pd.read_csv('data.csv')

def get_nested_rec(key, grp):
    rec = {}
    rec['PrimaryId'] = key[0]
    rec['FirstName'] = key[1]
    rec['LastName'] = key[2]
    rec['City'] = key[3]

    for field in ['CarName','DogName']:
        rec[field] = list(grp[field].unique())

    return rec

records = []
for key, grp in df.groupby(['PrimaryId','FirstName','LastName','City']):
    rec = get_nested_rec(key, grp)
    records.append(rec)

records = dict(data = records)

print(json.dumps(records, indent=4))

结果是：

{
    "data": [
        {
            "City": "NewYork", 
            "FirstName": "John", 
            "PrimaryId": 100, 
            "LastName": "Smith", 
            "CarName": [
                "Toyota", 
                "BMW"
            ], 
            "DogName": [
                "Spike", 
                "Rusty"
            ]
        }, 
        {
            "City": "Sydney", 
            "FirstName": "Ben", 
            "PrimaryId": 101, 
            "LastName": "Swan", 
            "CarName": [
                "Volkswagen", 
                "Ford", 
                "Audi"
            ], 
            "DogName": [
                "Buddy", 
                "Max"
            ]
        }, 
        {
            "City": "London", 
            "FirstName": "Julia", 
            "PrimaryId": 102, 
            "LastName": "Brown", 
            "CarName": [
                "Mini"
            ], 
            "DogName": [
                "Lucy"
            ]
        }
    ]
}

---

下面是使用

csv.DictReader

执行此操作的一般方法

从加载数据开始：

import csv
import itertools
with open('stuff.csv', 'rb') as csvfile:
    all_ = list(csv.DictReader(csvfile))

现在，您可以使用

itertools.groupby

对每个组进行分组和处理。比如说

d = []
for k, g in itertools.groupby(
        all_, 
        key=lambda r: (r['PrimaryId'], r[' LastName'])):
    d.append({
        'PrimaryId': k[0],
        'LastName': k[1],
        'CarName': [e[' CarName'] for e in g]
        })

将按主id和姓氏分组，并列出车辆列表

---

一旦你有了这样的东西，你就可以使用了。

下面是使用csv.DictReader的一般方法

从加载数据开始：

import csv
import itertools
with open('stuff.csv', 'rb') as csvfile:
    all_ = list(csv.DictReader(csvfile))

现在，您可以使用

itertools.groupby

对每个组进行分组和处理。比如说

d = []
for k, g in itertools.groupby(
        all_, 
        key=lambda r: (r['PrimaryId'], r[' LastName'])):
    d.append({
        'PrimaryId': k[0],
        'LastName': k[1],
        'CarName': [e[' CarName'] for e in g]
        })

将按主id和姓氏分组，并列出车辆列表

---

一旦你有了这样的东西，你可以直接使用。

请在这里发布你的代码，即你尝试了什么。此外，您的csv似乎有额外的空格，您的json也肯定不是json。请在此处发布您的代码，即您尝试了什么。此外，您的csv似乎有额外的空间，您的json也肯定不是json。