使用Pandas将嵌套CSV转换为嵌套JSON_Json_Python 3.x_Pandas_Dataframe_Csv

使用Pandas将嵌套CSV转换为嵌套JSON

json python-3.x pandas dataframe csv

使用Pandas将嵌套CSV转换为嵌套JSON,json,python-3.x,pandas,dataframe,csv,Json,Python 3.x,Pandas,Dataframe,Csv,我有一个这样的数据帧 org.iden.account,org.iden.id,adress.city,adress.country,person.name.fullname,person.gender,person.birthYear,subs.id,subs.subs1.birthday,subs.subs1.org.address.country,subs.subs1.org.address.strret1,subs.org.buyer.email.address,subs.org.buy

我有一个这样的数据帧

org.iden.account,org.iden.id,adress.city,adress.country,person.name.fullname,person.gender,person.birthYear,subs.id,subs.subs1.birthday,subs.subs1.org.address.country,subs.subs1.org.address.strret1,subs.org.buyer.email.address,subs.org.buyer.phone.number
account123,id123,riga,latvia,laura,female,1990,subs123,1990-12-14T00:00:00Z,latvia,street 1,email1@myorg.com|email2@sanoma.com,+371401234567
account123,id000,riga,latvia,laura,female,1990,subs456,1990-12-14T00:00:00Z,latvia,street 1,email1@myorg.com,+371401234567
account123,id456,riga,latvia,laura,female,1990,subs789,1990-12-14T00:00:00Z,latvia,street 1,email1@myorg.com,+371401234567

json_data = [
    {
        "org.iden.account": "account123",
        "org.iden.id": "id123",
        "adress.city": "riga",
        "adress.country": "latvia",
        "person.name.fullname": "laura",
        "person.gender": "female",
        "person.birthYear": 1990,
        "subs.id": "subs123",
        "subs.subs1.birthday": "1990-12-14T00:00:00Z",
        "subs.subs1.org.address.country": "latvia",
        "subs.subs1.org.address.strret1": "street 1",
        "subs.org.buyer.email.address": "email1@myorg.com|email2@sanoma.com",
        "subs.org.buyer.phone.number": 371401234567
    },
    {
        "org.iden.account": "account123",
        "org.iden.id": "id000",
        "adress.city": "riga",
        "adress.country": "latvia",
        "person.name.fullname": "laura",
        "person.gender": "female",
        "person.birthYear": 1990,
        "subs.id": "subs456",
        "subs.subs1.birthday": "1990-12-14T00:00:00Z",
        "subs.subs1.org.address.country": "latvia",
        "subs.subs1.org.address.strret1": "street 1",
        "subs.org.buyer.email.address": "email1@myorg.com",
        "subs.org.buyer.phone.number": 371407654321
    },
    {
        "org.iden.account": "account123",
        "org.iden.id": "id456",
        "adress.city": "riga",
        "adress.country": "latvia",
        "person.name.fullname": "laura",
        "person.gender": "female",
        "person.birthYear": 1990,
        "subs.id": "subs789",
        "subs.subs1.birthday": "1990-12-14T00:00:00Z",
        "subs.subs1.org.address.country": "latvia",
        "subs.subs1.org.address.strret1": "street 1",
        "subs.org.buyer.email.address": "email1@myorg.com",
        "subs.org.buyer.phone.number": 371407654321
    }
]

我需要将其转换为一个嵌套的JSON，该JSON基于以点（.）分隔的列。因此，对于第一行，预期结果应为

{
    "org": {
        "iden": {
            "account":  "account123",
            "id": "id123"
        }
    },
    "address": {
        "city": "riga",
        "country": "country"
    },
    "person": {
        "name": {
            "fullname": laura,
        },
        "gender": "female",
        "birthYear": 1990
    },
    "subs": {
        "id": "subs123",
        "subs1": {
            "birthday": "1990-12-14T00:00:00Z",
            "org": {
                "address": {
                    "country": "latvia",
                    "street1": "street 1"
                }
            }
        },
        "org": {
            "buyer": {
                "email": {
                    "address": "email1@myorg.com|email2@sanoma.com"
                },
            "phone": {
                "number": "+371401234567"
                }
            }
        }
    }

}

当然，所有的记录都是一个列表。我曾尝试使用simple pandas

.to_json（）
[{"org.iden.account":"account123","org.iden.id":"id123","adress.city":"riga","adress.country":"latvia","person.name.fullname":"laura","person.gender":"female","person.birthYear":1990,"subs.id":"subs123","subs.subs1.birthday":"1990-12-14T00:00:00Z","subs.subs1.org.address.country":"latvia","subs.subs1.org.address.strret1":"street 1","subs.org.buyer.email.address":"email1@myorg.com|email2@sanoma.com","subs.org.buyer.phone.number":371401234567},{"org.iden.account":"account123","org.iden.id":"id000","adress.city":"riga","adress.country":"latvia","person.name.fullname":"laura","person.gender":"female","person.birthYear":1990,"subs.id":"subs456","subs.subs1.birthday":"1990-12-14T00:00:00Z","subs.subs1.org.address.country":"latvia","subs.subs1.org.address.strret1":"street 1","subs.org.buyer.email.address":"email1@myorg.com","subs.org.buyer.phone.number":371407654321},{"org.iden.account":"account123","org.iden.id":"id456","adress.city":"riga","adress.country":"latvia","person.name.fullname":"laura","person.gender":"female","person.birthYear":1990,"subs.id":"subs789","subs.subs1.birthday":"1990-12-14T00:00:00Z","subs.subs1.org.address.country":"latvia","subs.subs1.org.address.strret1":"street 1","subs.org.buyer.email.address":"email1@myorg.com","subs.org.buyer.phone.number":371407654321}]

在此方面的任何帮助都将不胜感激
def df_to_json(row):
    tree = {}
    for item in row.index:
        t = tree
        for part in item.split('.'):
            prev, t = t, t.setdefault(part, {})
        prev[part] = row[item]
    return tree

假设您的json
结构如下所示
org.iden.account,org.iden.id,adress.city,adress.country,person.name.fullname,person.gender,person.birthYear,subs.id,subs.subs1.birthday,subs.subs1.org.address.country,subs.subs1.org.address.strret1,subs.org.buyer.email.address,subs.org.buyer.phone.number
account123,id123,riga,latvia,laura,female,1990,subs123,1990-12-14T00:00:00Z,latvia,street 1,email1@myorg.com|email2@sanoma.com,+371401234567
account123,id000,riga,latvia,laura,female,1990,subs456,1990-12-14T00:00:00Z,latvia,street 1,email1@myorg.com,+371401234567
account123,id456,riga,latvia,laura,female,1990,subs789,1990-12-14T00:00:00Z,latvia,street 1,email1@myorg.com,+371401234567


json_data = [
    {
        "org.iden.account": "account123",
        "org.iden.id": "id123",
        "adress.city": "riga",
        "adress.country": "latvia",
        "person.name.fullname": "laura",
        "person.gender": "female",
        "person.birthYear": 1990,
        "subs.id": "subs123",
        "subs.subs1.birthday": "1990-12-14T00:00:00Z",
        "subs.subs1.org.address.country": "latvia",
        "subs.subs1.org.address.strret1": "street 1",
        "subs.org.buyer.email.address": "email1@myorg.com|email2@sanoma.com",
        "subs.org.buyer.phone.number": 371401234567
    },
    {
        "org.iden.account": "account123",
        "org.iden.id": "id000",
        "adress.city": "riga",
        "adress.country": "latvia",
        "person.name.fullname": "laura",
        "person.gender": "female",
        "person.birthYear": 1990,
        "subs.id": "subs456",
        "subs.subs1.birthday": "1990-12-14T00:00:00Z",
        "subs.subs1.org.address.country": "latvia",
        "subs.subs1.org.address.strret1": "street 1",
        "subs.org.buyer.email.address": "email1@myorg.com",
        "subs.org.buyer.phone.number": 371407654321
    },
    {
        "org.iden.account": "account123",
        "org.iden.id": "id456",
        "adress.city": "riga",
        "adress.country": "latvia",
        "person.name.fullname": "laura",
        "person.gender": "female",
        "person.birthYear": 1990,
        "subs.id": "subs789",
        "subs.subs1.birthday": "1990-12-14T00:00:00Z",
        "subs.subs1.org.address.country": "latvia",
        "subs.subs1.org.address.strret1": "street 1",
        "subs.org.buyer.email.address": "email1@myorg.com",
        "subs.org.buyer.phone.number": 371407654321
    }
]

您可以在dict
的基础上嵌套它
def nestify(unnested):
    nested = dict()
    for k, v in unnested.items():
        current_dict = nested
        parts = k.split('.')
        for i in parts[:-1]:
            if i not in current_dict:
                current_dict[i] = dict()
            current_dict = current_dict[i]
        current_dict[parts[-1]] = v
    return nested

此函数获取一个未列出的dict
s，遍历键并将值指定给最终深度
评论版
def nestify(unnested):
    # this will be our return value
    nested = dict()
    for k, v in unnested.items():
        # current_dict is the current dict were operating on
        # gets reset to the base dict on each unnested key
        current_dict = nested
        parts = k.split('.')
        # only create dicts up to the final period
        # for example, current_dict is the base
        # and creates an empty dict under the org key
        # then current_dict is under the org key
        # and creates an empty dict under the iden key
        # then current_dict is under the iden key
        for i in parts[:-1]:
            # no reason to create an empty dict if it was
            # already created for a prior key
            if i not in current_dict:
                current_dict[i] = dict()
            current_dict = current_dict[i]
        # assign the value of the unnested dict
        # to each final current_dict
        # for example, the final part of the first key is "account"
        # so rather than assign an empty dict, assign it "account123" 
        current_dict[parts[-1]] = v
    return nested

然后，您可以在理解中对json_data
列表的每个元素调用它
nested = [nestify(i) for i in json_data]

完整代码：
json_data = [
    {
        "org.iden.account": "account123",
        "org.iden.id": "id123",
        "adress.city": "riga",
        "adress.country": "latvia",
        "person.name.fullname": "laura",
        "person.gender": "female",
        "person.birthYear": 1990,
        "subs.id": "subs123",
        "subs.subs1.birthday": "1990-12-14T00:00:00Z",
        "subs.subs1.org.address.country": "latvia",
        "subs.subs1.org.address.strret1": "street 1",
        "subs.org.buyer.email.address": "email1@myorg.com|email2@sanoma.com",
        "subs.org.buyer.phone.number": 371401234567
    },
    {
        "org.iden.account": "account123",
        "org.iden.id": "id000",
        "adress.city": "riga",
        "adress.country": "latvia",
        "person.name.fullname": "laura",
        "person.gender": "female",
        "person.birthYear": 1990,
        "subs.id": "subs456",
        "subs.subs1.birthday": "1990-12-14T00:00:00Z",
        "subs.subs1.org.address.country": "latvia",
        "subs.subs1.org.address.strret1": "street 1",
        "subs.org.buyer.email.address": "email1@myorg.com",
        "subs.org.buyer.phone.number": 371407654321
    },
    {
        "org.iden.account": "account123",
        "org.iden.id": "id456",
        "adress.city": "riga",
        "adress.country": "latvia",
        "person.name.fullname": "laura",
        "person.gender": "female",
        "person.birthYear": 1990,
        "subs.id": "subs789",
        "subs.subs1.birthday": "1990-12-14T00:00:00Z",
        "subs.subs1.org.address.country": "latvia",
        "subs.subs1.org.address.strret1": "street 1",
        "subs.org.buyer.email.address": "email1@myorg.com",
        "subs.org.buyer.phone.number": 371407654321
    }
]


def nestify(unnested):
    nested = dict()
    for k, v in unnested.items():
        current_dict = nested
        parts = k.split('.')
        for i in parts[:-1]:
            if i not in current_dict:
                current_dict[i] = dict()
            current_dict = current_dict[i]
        current_dict[parts[-1]] = v
    return nested

nested = [nestify(i) for i in json_data]
print(nested)

输出：
[
    {
        'adress': {
            'city': 'riga', 
            'country': 'latvia'
        },
        'org': {
            'iden': {
                'account': 'account123', 
                'id': 'id123'
            }
        },
        'person': {
            'birthYear': 1990,
            'gender': 'female',
            'name': {
                'fullname': 'laura'
            }
        },
        'subs': {
            'id': 'subs123',
            'org': {
                'buyer': {
                    'email': {
                        'address': 'email1@myorg.com|email2@sanoma.com'
                    },
                    'phone': {
                        'number': 371401234567
                    }
                }
            },
            'subs1': {
                'birthday': '1990-12-14T00:00:00Z',
                'org': {
                    'address': {
                        'country': 'latvia',
                        'strret1': 'street 1'
                    }
                }
            }
        }
    },
    {
        'adress': {
            'city': 'riga', 
            'country': 'latvia'
        },
        'org': {
            'iden': {
                'account': 'account123', 
                'id': 'id000'
            }
        },
        'person': {
            'birthYear': 1990,
            'gender': 'female',
            'name': {
                'fullname': 'laura'
            }
        },
        'subs': {
            'id': 'subs456',
            'org': {
                'buyer': {
                    'email': {
                        'address': 'email1@myorg.com'
                    },
                    'phone': {
                        'number': 371407654321
                    }
                }
            },
            'subs1': {
                'birthday': '1990-12-14T00:00:00Z',
                'org': {
                    'address': {
                        'country': 'latvia',
                        'strret1': 'street 1'
                    }
                }
            }
        }
    },
    {
        'adress': {
            'city': 'riga', 
            'country': 'latvia'
        },
        'org': {
            'iden': {
                'account': 'account123', 
                'id': 'id456'
            }
        },
        'person': {
            'birthYear': 1990,
            'gender': 'female',
            'name': {
                'fullname': 'laura'
            }
        },
        'subs': {
            'id': 'subs789',
            'org': {
                'buyer': {
                    'email': {
                        'address': 'email1@myorg.com'
                    },
                    'phone': {
                        'number': 371407654321
                    }
                }
            },
            'subs1': {
                'birthday': '1990-12-14T00:00:00Z',
                'org': {
                    'address': {
                        'country': 'latvia',
                        'strret1': 'street 1'
                    }
                }
            }
        }
    }
]

您是否可以使用直接处理json
数据而不是通过pandas
的解决方案？@Axe319当然可以。Pandas实际上在数据帧上提供了很多灵活性，但其他解决方案肯定是受欢迎的