从Python的嵌套字典到平面Pandas Dataframe

我有一个嵌套的字典的公共信息的就业历史的人，我想构建面板数据类似于下表。

这里是嵌套字典。
上表中人员1的嵌套字典如下所示。

{'basicInformation': {'individualId': 6092353,
'firstName': 'A','middleName': 'ANTHONY','lastName': 'OLIVETTI',
'otherNames': ['ALBERT A OLIVETTI',
'ALBERT ANTHONY OLIVETTI',
'ANTHONY A OLIVETTI',
'ANTHONY  OLIVETTI'],
'bcScope': 'Active',
'iaScope': 'Active',
'daysInIndustryCalculatedDate': '10/16/2013'},
'currentEmployments': [{'firmId': 8174,
'firmName': 'UBS FINANCIAL SERVICES INC.',
'iaOnly': 'N',
'registrationBeginDate': '10/17/2013',
'firmBCScope': 'ACTIVE',
'firmIAScope': 'ACTIVE',
'iaSECNumber': '7163',
'iaSECNumberType': '801',
'bdSECNumber': '16267',
'branchOfficeLocations': [{'locatedAtFlag': 'Y',
'supervisedFromFlag': 'N',
'privateResidenceFlag': 'N',
'branchOfficeId': '88789',
'street1': '1251 AVE OF THE AMERICAS',
'street2': '2ND FLOOR',
'city': 'NEW YORK',
'cityAlias': ['MANHATTAN',
'NEW YORK',
'NEW YORK CITY',
'NY',
'NY CITY',
'NYC'],
'state': 'NY',
'country': 'United States',
'zipCode': '10020',
'latitude': '40.758908',
'longitude': '-73.97902',
'geoLocation': '40.758908,-73.97902',
'nonRegisteredOfficeFlag': 'N',
'elaBeginDate': '07/15/2013'}]}],
'currentIAEmployments': [{'firmId': 8174,
'firmName': 'UBS FINANCIAL SERVICES INC.',
'iaOnly': 'Y',
'registrationBeginDate': '2/24/2014',
'firmBCScope': 'ACTIVE',
'firmIAScope': 'ACTIVE',
'iaSECNumber': '7163',
'iaSECNumberType': '801',
'bdSECNumber': '16267',
'branchOfficeLocations': [{'locatedAtFlag': 'Y',
 'supervisedFromFlag': 'N',
 'privateResidenceFlag': 'N',
 'branchOfficeId': '88789',
 'street1': '1251 AVE OF THE AMERICAS',
 'street2': '2ND FLOOR',
 'city': 'NEW YORK',
 'cityAlias': ['MANHATTAN',
  'NEW YORK',
  'NEW YORK CITY',
  'NY',
  'NY CITY',
  'NYC'],
 'state': 'NY',
 'country': 'United States',
 'zipCode': '10020',
 'latitude': '40.758908',
 'longitude': '-73.97902',
 'geoLocation': '40.758908,-73.97902',
 'nonRegisteredOfficeFlag': 'N',
 'elaBeginDate': '07/15/2013'}]}],
 'previousEmployments': [],
 'previousIAEmployments': [],
 'disclosureFlag': 'N',
 'iaDisclosureFlag': 'N',
 'disclosures': [],
 'examsCount': {'stateExamCount': 1,
 'principalExamCount': 0,
 'productExamCount': 3},
 'stateExamCategory': [{'examCategory': 'Series 66',
 'examName': 'Uniform Combined State Law Examination',
 'examTakenDate': '2/18/2014',
 'examScope': 'BOTH'}],
 'principalExamCategory': [],
 'productExamCategory': [{'examCategory': 'SIE',
 'examName': 'Securities Industry Essentials Examination',
 'examTakenDate': '10/1/2018',
 'examScope': 'BC'},
 {'examCategory': 'Series 3',
 'examName': 'National Commodity Futures Examination',
 'examTakenDate': '10/27/2014',
 'examScope': 'BC'},
 {'examCategory': 'Series 7',
 'examName': 'General Securities Representative Examination',
 'examTakenDate': '10/17/2013',
 'examScope': 'BC'}],
 'registrationCount': {'approvedSRORegistrationCount': 10,
 'approvedFinraRegistrationCount': 1,
 'approvedStateRegistrationCount': 7,
 'approvedIAStateRegistrationCount': 2},
 'registeredStates': [{'state': 'California',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '5/31/2022'},
 {'state': 'Connecticut',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '2/26/2014'},
 {'state': 'Florida',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '2/26/2014'},
 {'state': 'New Jersey',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '1/23/2014'},
 {'state': 'New Jersey',
 'regScope': 'IA',
 'status': 'APPROVED',
 'regDate': '2/24/2014'},
 {'state': 'New York',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '2/18/2014'},
 {'state': 'New York',
 'regScope': 'IA',
 'status': 'APPROVED',
 'regDate': '10/26/2021'},
 {'state': 'North Carolina',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '5/31/2022'},
 {'state': 'Pennsylvania',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '2/26/2014'}],
 'registeredSROs': [{'sro': 'BOX Exchange LLC', 'status': 'APPROVED'},
 {'sro': 'Cboe Exchange, Inc.', 'status': 'APPROVED'},
 {'sro': 'FINRA', 'status': 'APPROVED'},
 {'sro': 'NYSE American LLC', 'status': 'APPROVED'},
 {'sro': 'NYSE Arca, Inc.', 'status': 'APPROVED'},
 {'sro': 'NYSE Chicago, Inc.', 'status': 'APPROVED'},
 {'sro': 'Nasdaq ISE, LLC', 'status': 'APPROVED'},
 {'sro': 'Nasdaq PHLX LLC', 'status': 'APPROVED'},
 {'sro': 'Nasdaq Stock Market', 'status': 'APPROVED'},
 {'sro': 'New York Stock Exchange', 'status': 'APPROVED'}],
 'brokerDetails': {'hasBCComments': 'N',
 'hasIAComments': 'N',
 'legacyReportStatusDescription': 'Not Requested'}}

上表中人2的嵌套字典如下。

{'basicInformation': {'individualId': 2652161,
'firstName': 'ALBERT',
'middleName': 'B',
'lastName': 'HORMAN',
'otherNames': ['A B HORMAN', 'ALBERT WILLIAM HORMAN', 'BILL  HORMAN'],
'bcScope': 'Active',
'iaScope': 'Active',
'daysInIndustryCalculatedDate': '9/17/1995'},
'currentEmployments': [{'firmId': 7784,
'firmName': 'FIDELITY BROKERAGE SERVICES LLC',
'iaOnly': 'N',
'registrationBeginDate': '1/1/2008',
'firmBCScope': 'ACTIVE',
'firmIAScope': 'NOTINSCOPE',
'bdSECNumber': '23292',
'branchOfficeLocations': [{'locatedAtFlag': 'Y',
'supervisedFromFlag': 'N',
'privateResidenceFlag': 'N',
'branchOfficeId': '369366',
 'street1': '825 EAST 1180 SOUTH',
 'city': 'AMERICAN FORK',
 'cityAlias': ['AM FORK', 'AMERICAN FORK', 'HIGHLAND', 'TIMPANOGOS'],
 'state': 'UT',
 'country': 'United States',
 'zipCode': '84003',
 'latitude': '40.405984',
 'longitude': '-111.82903',
 'geoLocation': '40.405984,-111.82903',
 'nonRegisteredOfficeFlag': 'N',
 'elaBeginDate': '07/04/2022'}]}],
 'currentIAEmployments': [{'firmId': 288590,
 'firmName': 'FIDELITY PERSONAL AND WORKPLACE ADVISORS',
 'iaOnly': 'Y',
 'registrationBeginDate': '7/13/2018',
 'firmBCScope': 'NOTINSCOPE',
 'firmIAScope': 'ACTIVE',
 'iaSECNumber': '112027',
 'iaSECNumberType': '801',
 'branchOfficeLocations': [{'locatedAtFlag': 'Y',
 'supervisedFromFlag': 'N',
 'privateResidenceFlag': 'N',
 'street1': '245 SUMMER STREET, V2A',
 'city': 'BOSTON',
 'cityAlias': ['BOSTON'],
 'state': 'MA',
 'country': 'United States',
 'zipCode': '02210',
 'latitude': '42.346571',
 'longitude': '-71.039563',
 'geoLocation': '42.346571,-71.039563',
 'nonRegisteredOfficeFlag': 'Y',
 'elaBeginDate': '07/13/2018'}]}],
 'previousEmployments': [{'iaOnly': 'N',
 'bdSECNumber': '35097',
 'firmId': 17507,
 'firmName': 'FIDELITY INVESTMENTS INSTITUTIONAL SERVICES COMPANY, INC.',
 'street1': '49 NORTH 400 WEST',
  'city': 'SALT LAKE CITY',
  'state': 'UT',
  'zipCode': '84101',
  'registrationBeginDate': '1/3/2001',
 'registrationEndDate': '1/1/2008',
 'firmBCScope': 'ACTIVE',
 'firmIAScope': 'NOTINSCOPE'},
 {'iaOnly': 'N',
 'bdSECNumber': '23292',
  'firmId': 7784,
 'firmName': 'FIDELITY BROKERAGE SERVICES LLC',
 'street1': '900 SALEM STREET',
 'city': 'SMITHFIELD',
 'state': 'RI',
 'country': 'UNITED STATES',
 'zipCode': '02917',
 'registrationBeginDate': '9/18/1995',
 'registrationEndDate': '1/4/2001',
 'firmBCScope': 'ACTIVE',
 'firmIAScope': 'NOTINSCOPE'}],
 'previousIAEmployments': [{'iaOnly': 'Y',
 'iaSECNumber': '13243',
 'iaSECNumberType': '801',
 'firmId': 104555,
 'firmName': 'STRATEGIC ADVISERS LLC',
 'street1': '49 NORTH 400 WEST',
 'city': 'SALT LAKE CITY',
 'state': 'UT',
 'country': 'United States',
 'zipCode': '84101',
 'registrationBeginDate': '2/15/2008',
 'registrationEndDate': '7/13/2018',
 'firmBCScope': 'NOTINSCOPE',
 'firmIAScope': 'ACTIVE'}],
 'disclosureFlag': 'N',
 'iaDisclosureFlag': 'N',
 'disclosures': [],
 'examsCount': {'stateExamCount': 2,
 'principalExamCount': 0,
 'productExamCount': 2},
 'stateExamCategory': [{'examCategory': 'Series 66',
 'examName': 'Uniform Combined State Law Examination',
 'examTakenDate': '2/26/2008',
 'examScope': 'BOTH'},
 {'examCategory': 'Series 63',
 'examName': 'Uniform Securities Agent State Law Examination',
 'examTakenDate': '9/7/1995',
 'examScope': 'BC'}],
 'principalExamCategory': [],
 'productExamCategory': [{'examCategory': 'SIE',
 'examName': 'Securities Industry Essentials Examination',
 'examTakenDate': '10/1/2018',
 'examScope': 'BC'},
 {'examCategory': 'Series 7',
 'examName': 'General Securities Representative Examination',
 'examTakenDate': '9/16/1995',
 'examScope': 'BC'}],
 'registrationCount': {'approvedSRORegistrationCount': 2,
 'approvedFinraRegistrationCount': 1,
 'approvedStateRegistrationCount': 52,
 'approvedIAStateRegistrationCount': 2},
 'registeredStates': [{'state': 'Alabama',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '1/1/2008'},
 {'state': 'Alaska',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '1/1/2008'},
 {'state': 'Arizona',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '1/1/2008'},
 {'state': 'Arkansas',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '1/1/2008'},
 {'state': 'California',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '1/1/2008'},
 {'state': 'Colorado',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '1/1/2008'},
 {'state': 'Connecticut',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '1/1/2008'},
{'state': 'Delaware',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'District of Columbia',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Florida',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Georgia',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Hawaii',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Idaho',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Illinois',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Indiana',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Iowa',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Kansas',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Kentucky',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Louisiana',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
 {'state': 'Maine',
 'regScope': 'BC',
 'status': 'APPROVED',
 'regDate': '1/1/2008'},
 {'state': 'Maryland',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Massachusetts',
 'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Michigan',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Minnesota',
 'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Mississippi',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Missouri',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Montana',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
{'state': 'Nebraska',
'regScope': 'BC',
'status': 'APPROVED',
'regDate': '1/1/2008'},
  {'state': 'Nevada',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
   {'state': 'New Hampshire',
   'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'New Jersey',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'New Mexico',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'New York',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'North Carolina',
   'regScope': 'BC',
   'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'North Dakota',
  'regScope': 'BC',
   'status': 'APPROVED',
   'regDate': '1/1/2008'},
  {'state': 'Ohio',
  'regScope': 'BC',
   'status': 'APPROVED',
   'regDate': '1/1/2008'},
   {'state': 'Oklahoma',
   'regScope': 'BC',
   'status': 'APPROVED',
   'regDate': '1/1/2008'},
   {'state': 'Oregon',
   'regScope': 'BC',
   'status': 'APPROVED',
   'regDate': '1/1/2008'},
   {'state': 'Pennsylvania',
   'regScope': 'BC',
   'status': 'APPROVED',
   'regDate': '1/1/2008'},
   {'state': 'Puerto Rico',
   'regScope': 'BC',
   'status': 'APPROVED',
   'regDate': '1/1/2008'},
   {'state': 'Rhode Island',
   'regScope': 'BC',
   'status': 'APPROVED',
    'regDate': '1/1/2008'},
   {'state': 'South Carolina',
   'regScope': 'BC',
   'status': 'APPROVED',
   'regDate': '1/1/2008'},
   {'state': 'South Dakota',
   'regScope': 'BC',
   'status': 'APPROVED',
   'regDate': '1/1/2008'},
   {'state': 'Tennessee',
   'regScope': 'BC',
   'status': 'APPROVED',
   'regDate': '1/1/2008'},
   {'state': 'Texas',
    'regScope': 'BC',
   'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'Texas',
  'regScope': 'IA',
  'status': 'APPROVED_RES',
  'regDate': '7/13/2018'},
  {'state': 'Utah',
   'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'Utah',
  'regScope': 'IA',
  'status': 'APPROVED',
  'regDate': '7/13/2018'},
  {'state': 'Vermont',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'Virginia',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'Washington',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'West Virginia',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'Wisconsin',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'},
  {'state': 'Wyoming',
  'regScope': 'BC',
  'status': 'APPROVED',
  'regDate': '1/1/2008'}],
  'registeredSROs': [{'sro': 'FINRA', 'status': 'APPROVED'},
  {'sro': 'New York Stock Exchange', 'status': 'APPROVED'}],
  'brokerDetails': {'hasBCComments': 'N',
  'hasIAComments': 'N',
   'legacyReportStatusDescription': 'Not Requested'}}

我尝试做的是实现JSON规范化和JSON扁平化。我已经修改了person1和person2的代码

import pandas as pds
 from flatten_json import flatten
 import json 

 #person_json is what I stored each person JSON. There are 2 
 #persons here. Thus, I do this two times to flatten the nested 
 #dictionary.

 person_temp = pds.json_normalize(flatten(person_json))

 # This line of the code is credited to Mr.Timeless

 data_frame = (person_temp.set_axis(person_temp.columns.str.split("_", n=1, 
 expand=True), axis=1).stack(1).droplevel(0))

 data_frame

编辑1：添加data_frame的捕获照片
示例data_frame看起来像这样。我只显示了data_frame的一部分，因为维度等于111行乘16列。

我从上面的代码中得到的是一个 Dataframe 。然而，我试图设法构建面板数据，就像我展示的第一张捕获的照片一样。我在这里发现的问题是提取“年份”和“城市”，并将它们构建到（不平衡）面板数据集。
我该怎么做？
欢迎任何建议/意见。
非常感谢

我建议采取不同的方法。
首先，定义以下helper函数：

import pandas as pd

def flatten(data, new_data):
    for key, value in data.items():
        if isinstance(value, dict):
            flatten(value, new_data)
        if isinstance(value, str) or isinstance(value, int) or isinstance(value, list):
            new_data[key] = value
    return new_data

def deal_with_dicts(df, columns):
    for col in columns:
        df = pd.concat([df, pd.json_normalize(df[col])], axis=1)
        df = df.drop(columns=col)
    return df

def deal_with_duplicated_column_names(df):
    duplicates = {k: 1 for k in df.columns}
    new_cols = []
    for col in df.columns:
        if col in new_cols:
            new_cols.append(col + f"_{duplicates[col]}")
            duplicates[col] += 1
        else:
            new_cols.append(col)
    df.columns = new_cols
    return df

然后：

from collections import defaultdict

person1_data = flatten(person1, defaultdict(list))
df = pd.json_normalize(person1_data)

# ROUND 1
for col in df.columns:
    df = df.explode(col)  # Deal with lists of dicts
df = df.reset_index(drop=True)
df = deal_with_dicts(
    df,
    [
        "currentEmployments",
        "currentIAEmployments",
        "stateExamCategory",
        "productExamCategory",
        "registeredStates",
        "registeredSROs",
    ],
)
df = deal_with_duplicated_column_names(df)

# ROUND 2
for col in df.columns:
    df = df.explode(col)  # Deal with lists of dicts
df = df.reset_index(drop=True)
df = deal_with_dicts(df, ["branchOfficeLocations", "branchOfficeLocations_1"])
df = deal_with_duplicated_column_names(df)

# ROUND 3
for col in df.columns:
    df = df.explode(col)  # Deal with lists of dicts
df = df.reset_index(drop=True)

它为您提供了person1字典中的所有数据作为展平的数据框架：

print(df.info())
# Output

[38880 rows x 88 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 38880 entries, 0 to 38879
Data columns (total 88 columns):
 #   Column                            Non-Null Count  Dtype
---  ------                            --------------  -----
 0   individualId                      38880 non-null  int64
 1   firstName                         38880 non-null  object
 2   middleName                        38880 non-null  object
 3   lastName                          38880 non-null  object
 4   otherNames                        38880 non-null  object
 5   bcScope                           38880 non-null  object
 6   iaScope                           38880 non-null  object
 7   daysInIndustryCalculatedDate      38880 non-null  object
 8   previousEmployments               0 non-null      object
 9   previousIAEmployments             0 non-null      object
 10  disclosureFlag                    38880 non-null  object
 11  iaDisclosureFlag                  38880 non-null  object
 12  disclosures                       0 non-null      object
 13  stateExamCount                    38880 non-null  int64
 14  principalExamCount                38880 non-null  int64
 15  productExamCount                  38880 non-null  int64
 16  principalExamCategory             0 non-null      object
 17  approvedSRORegistrationCount      38880 non-null  int64
 18  approvedFinraRegistrationCount    38880 non-null  int64
 19  approvedStateRegistrationCount    38880 non-null  int64
 20  approvedIAStateRegistrationCount  38880 non-null  int64
 21  hasBCComments                     38880 non-null  object
 22  hasIAComments                     38880 non-null  object
 23  legacyReportStatusDescription     38880 non-null  object
 24  firmId                            38880 non-null  int64
 25  firmName                          38880 non-null  object
 26  iaOnly                            38880 non-null  object
 27  registrationBeginDate             38880 non-null  object
 28  firmBCScope                       38880 non-null  object
 29  firmIAScope                       38880 non-null  object
 30  iaSECNumber                       38880 non-null  object
 31  iaSECNumberType                   38880 non-null  object
 32  bdSECNumber                       38880 non-null  object
 33  firmId_1                          38880 non-null  int64
 34  firmName_1                        38880 non-null  object
 35  iaOnly_1                          38880 non-null  object
 36  registrationBeginDate_1           38880 non-null  object
 37  firmBCScope_1                     38880 non-null  object
 38  firmIAScope_1                     38880 non-null  object
 39  iaSECNumber_1                     38880 non-null  object
 40  iaSECNumberType_1                 38880 non-null  object
 41  bdSECNumber_1                     38880 non-null  object
 42  examCategory                      38880 non-null  object
 43  examName                          38880 non-null  object
 44  examTakenDate                     38880 non-null  object
 45  examScope                         38880 non-null  object
 46  examCategory_1                    38880 non-null  object
 47  examName_1                        38880 non-null  object
 48  examTakenDate_1                   38880 non-null  object
 49  examScope_1                       38880 non-null  object
 50  state                             38880 non-null  object
 51  regScope                          38880 non-null  object
 52  status                            38880 non-null  object
 53  regDate                           38880 non-null  object
 54  sro                               38880 non-null  object
 55  status_1                          38880 non-null  object
 56  locatedAtFlag                     38880 non-null  object
 57  supervisedFromFlag                38880 non-null  object
 58  privateResidenceFlag              38880 non-null  object
 59  branchOfficeId                    38880 non-null  object
 60  street1                           38880 non-null  object
 61  street2                           38880 non-null  object
 62  city                              38880 non-null  object
 63  cityAlias                         38880 non-null  object
 64  state_1                           38880 non-null  object
 65  country                           38880 non-null  object
 66  zipCode                           38880 non-null  object
 67  latitude                          38880 non-null  object
 68  longitude                         38880 non-null  object
 69  geoLocation                       38880 non-null  object
 70  nonRegisteredOfficeFlag           38880 non-null  object
 71  elaBeginDate                      38880 non-null  object
 72  locatedAtFlag_1                   38880 non-null  object
 73  supervisedFromFlag_1              38880 non-null  object
 74  privateResidenceFlag_1            38880 non-null  object
 75  branchOfficeId_1                  38880 non-null  object
 76  street1_1                         38880 non-null  object
 77  street2_1                         38880 non-null  object
 78  city_1                            38880 non-null  object
 79  cityAlias_1                       38880 non-null  object
 80  state_2                           38880 non-null  object
 81  country_1                         38880 non-null  object
 82  zipCode_1                         38880 non-null  object
 83  latitude_1                        38880 non-null  object
 84  longitude_1                       38880 non-null  object
 85  geoLocation_1                     38880 non-null  object
 86  nonRegisteredOfficeFlag_1         38880 non-null  object
 87  elaBeginDate_1                    38880 non-null  object
dtypes: int64(10), object(78)
memory usage: 26.1+ MB

从Python的嵌套字典到平面Pandas Dataframe

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