1T_접속하기



In [1]:

    
from pymongo import MongoClient



In [2]:

    
client = MongoClient(
    "mongodb://mongodb.dobest.io:27017/"
)



In [3]:

    
client.database_names()









    Out[3]:





['MJ',
 'ansuchan',
 'blinkie',
 'cykim',
 'dglee',
 'dglee2',
 'dobestan',
 'dobestan09',
 'donghok',
 'donghokk',
 'eunhee08',
 'eunhee09',
 'fastmanager',
 'googler',
 'hani0808',
 'hani0810',
 'haniserver',
 'heee',
 'hotsummer',
 'hotsummmer',
 'hun',
 'hyunwoo',
 'jackass',
 'jkpark',
 'jm',
 'johnnykoo',
 'jtkim',
 'kpy',
 'kt',
 'kwangho',
 'local',
 'mh',
 'moon',
 'moonmoon',
 'nodecamp10',
 'paulsoh',
 'psh',
 'samuel',
 'sandoreo',
 'sandoreo7',
 'sean',
 'seo2',
 'sheen',
 'sjhong',
 'sooylee',
 'soullovers',
 'suchan',
 'suchanan',
 'sun',
 'sunmee',
 'taiwoo',
 'test',
 'wonhee',
 'xyky',
 'xykyMovie',
 'yekyuchoi',
 'yeoyou',
 'yonghyun',
 'yykim']



In [4]:

    
client2 = MongoClient(
    "mongodb://kipoy:rlarlvy@ds013024.mlab.com:13024/kkpoy"
    # username: kipoy
    # password: rlarlvy
    # database name: kkpoy
)



In [5]:

    
client2









    Out[5]:





MongoClient(host=['ds013024.mlab.com:13024'], document_class=dict, tz_aware=False, connect=True)



In [6]:

    
client2.database_names()   #관리자 권한이 없기 때문에 안된다.









    



---------------------------------------------------------------------------
OperationFailure                          Traceback (most recent call last)
<ipython-input-6-4e0d25cc30fc> in <module>()
----> 1 client2.database_names()   #관리자 권한이 없기 때문에 안된다.

C:\Anaconda3\lib\site-packages\pymongo\mongo_client.py in database_names(self)
   1086         return [db["name"] for db in
   1087                 self._database_default_options('admin').command(
-> 1088                     "listDatabases")["databases"]]
   1089 
   1090     def drop_database(self, name_or_database):

C:\Anaconda3\lib\site-packages\pymongo\database.py in command(self, command, value, check, allowable_errors, read_preference, codec_options, **kwargs)
    479             return self._command(sock_info, command, slave_ok, value,
    480                                  check, allowable_errors, read_preference,
--> 481                                  codec_options, **kwargs)
    482 
    483     def _list_collections(self, sock_info, slave_okay, criteria=None):

C:\Anaconda3\lib\site-packages\pymongo\database.py in _command(self, sock_info, command, slave_ok, value, check, allowable_errors, read_preference, codec_options, **kwargs)
    391                                  codec_options,
    392                                  check,
--> 393                                  allowable_errors)
    394 
    395     def command(self, command, value=1, check=True,

C:\Anaconda3\lib\site-packages\pymongo\pool.py in command(self, dbname, spec, slave_ok, read_preference, codec_options, check, allowable_errors, check_keys, read_concern)
    237                            check, allowable_errors, self.address,
    238                            check_keys, self.listeners, self.max_bson_size,
--> 239                            read_concern)
    240         except OperationFailure:
    241             raise

C:\Anaconda3\lib\site-packages\pymongo\network.py in command(sock, dbname, spec, slave_ok, is_mongos, read_preference, codec_options, check, allowable_errors, address, check_keys, listeners, max_bson_size, read_concern)
    100         response_doc = unpacked['data'][0]
    101         if check:
--> 102             helpers._check_command_response(response_doc, None, allowable_errors)
    103     except Exception as exc:
    104         if publish:

C:\Anaconda3\lib\site-packages\pymongo\helpers.py in _check_command_response(response, msg, allowable_errors)
    203 
    204             msg = msg or "%s"
--> 205             raise OperationFailure(msg % errmsg, code, response)
    206 
    207 

OperationFailure: not authorized on admin to execute command { listDatabases: 1 }



In [7]:

    
client2.kkpoy.collection_names()









    Out[7]:





['kimkipoy', 'system.indexes', 'users']



In [8]:

    
client2.kkpoy.users.find_one()   #원래는 JSON인데 dict형태로 뽑혔다. pymongo가 다 바꾸어주었다.









    Out[8]:





{'_id': ObjectId('57e0cce4916d7a87056c54e1'),
 'age': 29,
 'email': 'kimkipoy@gmail.com',
 'name': 'KiPyo Kim',
 'pets': ['dog', 'cat', 'bird']}

2T_데이터 추가하기, 검색하기



In [9]:

    
db = client2.kkpoy
# db = client["kkpoy"]
#위 아래 같은 의미



In [10]:

    
users = db.users
# users = db["usres"]
#위 아래 같은 의미



In [12]:

    
users   #users가 collection









    Out[12]:





Collection(Database(MongoClient(host=['ds013024.mlab.com:13024'], document_class=dict, tz_aware=False, connect=True), 'kkpoy'), 'users')



In [13]:

    
users.find_one()









    Out[13]:





{'_id': ObjectId('57e0cce4916d7a87056c54e1'),
 'age': 29,
 'email': 'kimkipoy@gmail.com',
 'name': 'KiPyo Kim',
 'pets': ['dog', 'cat', 'bird']}



In [14]:

    
MONGO_QUERY = {} # json 으로 query 를 날린다. ( dict => json; pymongo )

users.find_one(MONGO_QUERY)









    Out[14]:





{'_id': ObjectId('57e0cce4916d7a87056c54e1'),
 'age': 29,
 'email': 'kimkipoy@gmail.com',
 'name': 'KiPyo Kim',
 'pets': ['dog', 'cat', 'bird']}



In [15]:

    
MONGO_QUERY = {}
users.find(MONGO_QUERY)









    Out[15]:





<pymongo.cursor.Cursor at 0xb42e780>



In [19]:

    
MONGO_QUERY = {}
cursor = users.find(MONGO_QUERY)



In [17]:

    
for user in cursor:
    print(user)









    



{'name': 'KiPyo Kim', 'age': 29, '_id': ObjectId('57e0cce4916d7a87056c54e1'), 'email': 'kimkipoy@gmail.com', 'pets': ['dog', 'cat', 'bird']}
{'name': 'Dani Kim', '_id': ObjectId('57e0d0f4916d7a87056c54e8'), 'email': 'kdn@naver.com'}



In [20]:

    
[
    user
    for user
    in cursor
]









    Out[20]:





[{'_id': ObjectId('57e0cce4916d7a87056c54e1'),
  'age': 29,
  'email': 'kimkipoy@gmail.com',
  'name': 'KiPyo Kim',
  'pets': ['dog', 'cat', 'bird']},
 {'_id': ObjectId('57e0d0f4916d7a87056c54e8'),
  'email': 'kdn@naver.com',
  'name': 'Dani Kim'}]



In [21]:

    
MONGO_QUERY = {"name": "Dani Kim"}

users.find_one(MONGO_QUERY)









    Out[21]:





{'_id': ObjectId('57e0d0f4916d7a87056c54e8'),
 'email': 'kdn@naver.com',
 'name': 'Dani Kim'}

find_찾을 때

insert_넣을 때



In [22]:

    
user = {
    "name": "이동규",
    "email": "milk@gmail.com",
}
users.insert_one(user)









    Out[22]:





<pymongo.results.InsertOneResult at 0xb41c3f0>



In [23]:

    
users_list = [
    {
        "name": "이영은",
        "phone": "01022706512",
    },
    {
        "name": "유영수",
        "email": "yskk@naver.com"
    }
]
users.insert_many(users_list)









    Out[23]:





<pymongo.results.InsertManyResult at 0xb41cd38>



In [32]:

    
watcha = client2.kipoy["watcha"]



In [37]:

    
watcha = db.watcha



In [26]:

    
import requests



In [27]:

    
response = requests.get("https://watcha.net/home/news.json?page=1&per=300")



In [28]:

    
watcha_news = response.json().get("news") # List



In [38]:

    
watcha.insert_many(watcha_news)









    Out[38]:





<pymongo.results.InsertManyResult at 0xb76e438>

직방 Collection => 데이터를 100개 정도 넣어보기



In [39]:

    
import pandas as pd
import requests
import json



In [40]:

    
zigbang = client2.kipoy["zigbang"]



In [41]:

    
zigbang = db.zigbang



In [42]:

    
zigbang









    Out[42]:





Collection(Database(MongoClient(host=['ds013024.mlab.com:13024'], document_class=dict, tz_aware=False, connect=True), 'kkpoy'), 'zigbang')



In [43]:

    
response = requests.get("https://api.zigbang.com/v1/items?detail=true&item_ids=4620292&item_ids=4366382&item_ids=4566963&item_ids=4585208&item_ids=4560308&item_ids=4552724&item_ids=4344484&item_ids=4612042&item_ids=4574810&item_ids=4588687&item_ids=4387287&item_ids=4538842&item_ids=4557985&item_ids=4579464&item_ids=4607349&item_ids=4603203&item_ids=4341393&item_ids=4575315&item_ids=4350877&item_ids=4538375&item_ids=4616443&item_ids=4281504&item_ids=4556024&item_ids=4550034&item_ids=4512172&item_ids=4507118&item_ids=4606156&item_ids=4457169&item_ids=4526327&item_ids=4407071&item_ids=4582264&item_ids=4607937&item_ids=4395275&item_ids=4568603&item_ids=4569329&item_ids=4564865&item_ids=4551098&item_ids=4617261&item_ids=4536918&item_ids=4614718&item_ids=4614198&item_ids=4610604&item_ids=4578711&item_ids=4593621&item_ids=4612621&item_ids=4518874&item_ids=4533169&item_ids=4409063&item_ids=4617602&item_ids=4477945&item_ids=4249606&item_ids=4560223&item_ids=4570020&item_ids=4517907&item_ids=4530774&item_ids=4525210&item_ids=4596138&item_ids=4588994&item_ids=4612357&item_ids=4411862")



In [44]:

    
zigbang_dict = response.json()



In [45]:

    
len(zigbang_dict)









    Out[45]:





3



In [46]:

    
zigbang_dict.keys()









    Out[46]:





dict_keys(['count_direct', 'count_agent', 'items'])



In [47]:

    
zigbang_items_list = zigbang_dict.get("items")



In [48]:

    
len(zigbang_items_list)









    Out[48]:





19



In [49]:

    
zigbang.insert_many(zigbang_items_list)









    Out[49]:





<pymongo.results.InsertManyResult at 0xb6b20d8>



In [50]:

    
MONGO_QUERY = {}
zigbang.find(MONGO_QUERY).count()
#SELECT COUNT(*) FROM ____;









    Out[50]:





19

보증금이 딱 1000만원인 매물 리스트를 출력해보자



In [51]:

    
MONGO_QUERY = {}
cursor = zigbang.find(MONGO_QUERY)
items_list = [
    document
    for document
    in cursor
    if document.get("item").get("deposit") == 1000
]



In [52]:

    
len(items_list)









    Out[52]:





2



In [53]:

    
MONGO_QUERY = {"title": "서울시 관악구 신림동"}
zigbang.find_one(MONGO_QUERY)









    Out[53]:





{'_id': ObjectId('57e0d97670147e1460111488'),
 'header': False,
 'header_height': 0,
 'item': {'address1': '서울시 관악구 신림동',
  'address2': '95-81',
  'address3': None,
  'agent_address1': '서울특별시 관악구 관악로15길 23 (봉천동)',
  'agent_comment': '',
  'agent_email': 'sky_rendi@naver.com',
  'agent_local1': '서울시',
  'agent_local2': '관악구',
  'agent_mobile': '0507-1280-7420',
  'agent_name': '부동산매거진공인중개사(김기영)',
  'agent_phone': '02-883-2482',
  'bjd_code': '1162010200',
  'bonbun_code': '95',
  'bubun_code': '81',
  'building': None,
  'building_type': '다세대건물',
  'contract': '서울특별시',
  'deposit': 5000,
  'description': '* 보증금 조정가 3000~5000\n* 채광 환기 굿\n* 대로가에서 살짝 골목\n* 주인거주\n* 날짜협의\n* 신축급건물',
  'description_og': '* 보증금 조정가 3000~5000\n* 채광 환기 굿\n* 대로가에서 살짝 골목\n* 주인거주\n* 날짜협의\n* 신축급건물',
  'elevator': '있음',
  'floor': '2층',
  'floor_all': '6층',
  'id': 4566963,
  'images': [{'count': 1,
    'index': 0,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/63a8c71f3919122a354b423e473d1f3258bda6a4.JPG'},
   {'count': 2,
    'index': 1,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/c619d0691394a1ff8169be5b42e3dc5f42451b79.JPG'},
   {'count': 3,
    'index': 2,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/c0361ee3fe0a421d77f30bfc6c59d378ea39f469.JPG'},
   {'count': 4,
    'index': 3,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/feea590f997954beca718898e8bea16fc27468a8.JPG'},
   {'count': 5,
    'index': 4,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/92e767198cb745994dae0b33d8a9d3e401147106.JPG'},
   {'count': 6,
    'index': 5,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/ba679beeb823a1e4e286eccea9d8fa056dba4722.JPG'},
   {'count': 7,
    'index': 6,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/08201dff33a0434922b333e88e95974c9f6fcbd7.JPG'},
   {'count': 8,
    'index': 7,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/14c8838181e0d90e19615b190251089ade0d801f.JPG'},
   {'count': 9,
    'index': 8,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/9747dae621994e1644f9642ceded7458bdea7d5f.JPG'},
   {'count': 10,
    'index': 9,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/e9472c27d98aa9ea1ff71b8a5b91bdecc9417106.JPG'},
   {'count': 11,
    'index': 10,
    'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/809b3ea25733d4052c7067bcb31f694f48816193.JPG'}],
  'images_count': 11,
  'images_thumbnail': '',
  'is_deposit_only': False,
  'is_direct': False,
  'is_owner': None,
  'is_premium': True,
  'is_premium2': False,
  'is_realestate': True,
  'is_room': False,
  'is_status_close': True,
  'is_status_open': False,
  'is_type_room': False,
  'is_zzim': False,
  'loan_text': '확인필요',
  'local1': '서울시',
  'local2': '관악구',
  'local3': '신림동',
  'manage_cost': '6만원',
  'manage_cost_inc': '수도, 인터넷, TV',
  'movein_date': '날짜협의',
  'near_subways': '봉천역(2호선), 신림역(2호선), 서울대입구역(2호선)',
  'options': '에어컨, 냉장고, 세탁기, 인덕션, 책상, 책장, 옷장, 신발장, 싱크대',
  'original_user_phone': '010-4107-0987',
  'parking': '가능',
  'pets_text': '확인필요',
  'profile_url': None,
  'random_location': '37.4769270541086,126.939279451312',
  'read_updated_at': '1/1/0001 12:00:00 AM',
  'rent': 20,
  'room_direction_text': '확인필요',
  'room_gubun_code': '01',
  'room_type': '원룸(분리형)',
  'secret_memo': None,
  'size': 8.0,
  'size_contract': 0.0,
  'size_m2': '26.45',
  'size_m2_contract': '-',
  'status': '광고종료',
  'title': '☎ 신축급 분리형원룸, 보증금 조정가, 날짜협의, 주인거주',
  'updated_at': '3개월 전',
  'user_email': 'lasolitudine@nate.com',
  'user_has_no_penalty': True,
  'user_has_penalty': False,
  'user_mobile': '0507-1280-8188',
  'user_name': '중개보조원(오종섭)',
  'user_no': 814905,
  'user_phone': '0507-1280-8188',
  'view_count': 201},
 'title': '서울시 관악구 신림동'}



In [54]:

    
MONGO_QUERY = {"item.deposit": 1000}   #관계형 DB의 관점에서는 말도 안되는 기능. 쉽게 찾을 수 있다.
zigbang.find(MONGO_QUERY).count()









    Out[54]:





2

보증금이 1000만원 이상인 매물 가져오기



In [56]:

    
MONGO_QUERY = {}
cursor = zigbang.find(MONGO_QUERY)

items_list = [
    document
    for document
    in cursor
    if document.get("item").get("deposit") >= 1000
]
len(items_list)









    Out[56]:





16



In [58]:

    
MONGO_QUERY = {"item.deposit": {"$gte": 1000}}   #1000보다 크거나 같다. greater than equal
zigbang.find(MONGO_QUERY).count()









    Out[58]:





16



In [59]:

    
MONGO_QUERY = {"item.deposit": {"$gte": 1000, "$lte": 2000}} 
zigbang.find(MONGO_QUERY).count()









    Out[59]:





6

보증금이 2000만원 이하이고 월세가 50만원 이하인 매물 출력하기



In [60]:

    
MONGO_QUERY = {}
cursor = zigbang.find(MONGO_QUERY)

items_list = [
    document
    for document
    in cursor
    if document.get("item").get("deposit") <= 2000 and document.get("item").get("rent") <= 50
]
len(items_list)









    Out[60]:





8



In [61]:

    
MONGO_QUERY = {
    "item.deposit": {"$lte": 2000},
    "item.rent": {"$lte": 50},
    }
zigbang.find(MONGO_QUERY).count()









    Out[61]:





8



In [62]:

    
zigbang_list = zigbang_dict.get("items")



In [63]:

    
len(zigbang_list)









    Out[63]:





19



In [64]:

    
new_zigbang_list = [
    item.get("item")
    for item
    in zigbang_list
]



In [65]:

    
new_zigbang_list[0]









    Out[65]:





{'address1': '서울시 관악구 신림동',
 'address2': '95-81',
 'address3': None,
 'agent_address1': '서울특별시 관악구 관악로15길 23 (봉천동)',
 'agent_comment': '',
 'agent_email': 'sky_rendi@naver.com',
 'agent_local1': '서울시',
 'agent_local2': '관악구',
 'agent_mobile': '0507-1280-7420',
 'agent_name': '부동산매거진공인중개사(김기영)',
 'agent_phone': '02-883-2482',
 'bjd_code': '1162010200',
 'bonbun_code': '95',
 'bubun_code': '81',
 'building': None,
 'building_type': '다세대건물',
 'contract': '서울특별시',
 'deposit': 5000,
 'description': '* 보증금 조정가 3000~5000\n* 채광 환기 굿\n* 대로가에서 살짝 골목\n* 주인거주\n* 날짜협의\n* 신축급건물',
 'description_og': '* 보증금 조정가 3000~5000\n* 채광 환기 굿\n* 대로가에서 살짝 골목\n* 주인거주\n* 날짜협의\n* 신축급건물',
 'elevator': '있음',
 'floor': '2층',
 'floor_all': '6층',
 'id': 4566963,
 'images': [{'count': 1,
   'index': 0,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/63a8c71f3919122a354b423e473d1f3258bda6a4.JPG'},
  {'count': 2,
   'index': 1,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/c619d0691394a1ff8169be5b42e3dc5f42451b79.JPG'},
  {'count': 3,
   'index': 2,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/c0361ee3fe0a421d77f30bfc6c59d378ea39f469.JPG'},
  {'count': 4,
   'index': 3,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/feea590f997954beca718898e8bea16fc27468a8.JPG'},
  {'count': 5,
   'index': 4,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/92e767198cb745994dae0b33d8a9d3e401147106.JPG'},
  {'count': 6,
   'index': 5,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/ba679beeb823a1e4e286eccea9d8fa056dba4722.JPG'},
  {'count': 7,
   'index': 6,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/08201dff33a0434922b333e88e95974c9f6fcbd7.JPG'},
  {'count': 8,
   'index': 7,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/14c8838181e0d90e19615b190251089ade0d801f.JPG'},
  {'count': 9,
   'index': 8,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/9747dae621994e1644f9642ceded7458bdea7d5f.JPG'},
  {'count': 10,
   'index': 9,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/e9472c27d98aa9ea1ff71b8a5b91bdecc9417106.JPG'},
  {'count': 11,
   'index': 10,
   'url': 'http://i1.zigbang.com/ic/users/814905/photos/uploads/809b3ea25733d4052c7067bcb31f694f48816193.JPG'}],
 'images_count': 11,
 'images_thumbnail': '',
 'is_deposit_only': False,
 'is_direct': False,
 'is_owner': None,
 'is_premium': True,
 'is_premium2': False,
 'is_realestate': True,
 'is_room': False,
 'is_status_close': True,
 'is_status_open': False,
 'is_type_room': False,
 'is_zzim': False,
 'loan_text': '확인필요',
 'local1': '서울시',
 'local2': '관악구',
 'local3': '신림동',
 'manage_cost': '6만원',
 'manage_cost_inc': '수도, 인터넷, TV',
 'movein_date': '날짜협의',
 'near_subways': '봉천역(2호선), 신림역(2호선), 서울대입구역(2호선)',
 'options': '에어컨, 냉장고, 세탁기, 인덕션, 책상, 책장, 옷장, 신발장, 싱크대',
 'original_user_phone': '010-4107-0987',
 'parking': '가능',
 'pets_text': '확인필요',
 'profile_url': None,
 'random_location': '37.4769270541086,126.939279451312',
 'read_updated_at': '1/1/0001 12:00:00 AM',
 'rent': 20,
 'room_direction_text': '확인필요',
 'room_gubun_code': '01',
 'room_type': '원룸(분리형)',
 'secret_memo': None,
 'size': 8.0,
 'size_contract': 0.0,
 'size_m2': '26.45',
 'size_m2_contract': '-',
 'status': '광고종료',
 'title': '☎ 신축급 분리형원룸, 보증금 조정가, 날짜협의, 주인거주',
 'updated_at': '3개월 전',
 'user_email': 'lasolitudine@nate.com',
 'user_has_no_penalty': True,
 'user_has_penalty': False,
 'user_mobile': '0507-1280-8188',
 'user_name': '중개보조원(오종섭)',
 'user_no': 814905,
 'user_phone': '0507-1280-8188',
 'view_count': 201}



In [66]:

    
zigbang.insert_many(new_zigbang_list)









    Out[66]:





<pymongo.results.InsertManyResult at 0xb6b2750>



In [67]:

    
MONGO_QUERY = {"deposit": {"$lte": 1000}}
zigbang.find(MONGO_QUERY).count()









    Out[67]:





5

비정형데이터에서 중요한 것은 어떤 데이터를 잘 넣을까 하는 부분에 대해서 고민해보아야 한다.
왜냐하면 있는 그대로 넣었을 경우에 비효율적인 부분이 발생하기 때문이다.

3T_Aggregate ( GROUP BY )

GROUP BY ... 지역별 숫자 세기 / 지역별 평균 보증금,월세 ( MongoDB )
World => MongoDB ( 데이터 전처리 ... )

aggregate ( GROUP BY )
zigbang ... address1을 기준으로 나누어보자(그룹을 나누어보자)



In [68]:

    
import pymongo

#GROUP BY
cursor = zigbang.aggregate([
        {
            "$group": {
                "_id": "$address1",
            }
        }
    ])
for document in cursor:
    print(document)









    



{'_id': '서울시 동작구 신대방1동'}
{'_id': '서울시 금천구 독산동'}
{'_id': '서울시 구로구 구로3동'}
{'_id': '서울시 동작구 상도동'}
{'_id': '서울시 관악구 봉천동'}
{'_id': '서울시 관악구 신림동'}



In [69]:

    
cursor = zigbang.aggregate([
        {
            "$group": {
                "_id": "$address1",
                "count": {"$sum": 1}
            }
        }
    ])
for document in cursor:
    print(document)









    



{'_id': '서울시 동작구 신대방1동', 'count': 1}
{'_id': '서울시 금천구 독산동', 'count': 1}
{'_id': '서울시 구로구 구로3동', 'count': 1}
{'_id': '서울시 동작구 상도동', 'count': 1}
{'_id': '서울시 관악구 봉천동', 'count': 6}
{'_id': '서울시 관악구 신림동', 'count': 9}



In [70]:

    
cursor = zigbang.aggregate([
        {
            "$group": {
                "_id": "$address1",
                "count": {"$sum": 1},
                "total deposit": {"$sum": "$deposit"}
            }
        }
    ])
for document in cursor:
    print(document)









    



{'_id': '서울시 동작구 신대방1동', 'count': 1, 'total deposit': 2000}
{'_id': '서울시 금천구 독산동', 'count': 1, 'total deposit': 10000}
{'_id': '서울시 구로구 구로3동', 'count': 1, 'total deposit': 300}
{'_id': '서울시 동작구 상도동', 'count': 1, 'total deposit': 500}
{'_id': '서울시 관악구 봉천동', 'count': 6, 'total deposit': 44800}
{'_id': '서울시 관악구 신림동', 'count': 9, 'total deposit': 72000}



In [71]:

    
cursor = zigbang.aggregate([
        {
            "$group": {
                "_id": "$address1",
                "count": {"$sum": 1},
                "average rent": {"$avg": "$rent"},
            }
        }
    ])
for document in cursor:
    print(document)









    



{'count': 1, '_id': '서울시 동작구 신대방1동', 'average rent': 35.0}
{'count': 1, '_id': '서울시 금천구 독산동', 'average rent': 0.0}
{'count': 1, '_id': '서울시 구로구 구로3동', 'average rent': 40.0}
{'count': 1, '_id': '서울시 동작구 상도동', 'average rent': 29.0}
{'count': 6, '_id': '서울시 관악구 봉천동', 'average rent': 24.166666666666668}
{'count': 9, '_id': '서울시 관악구 신림동', 'average rent': 18.666666666666668}



In [72]:

    
cursor = zigbang.aggregate([
        {
            "$group": {
                "_id": "$address1",
                "count": {"$sum": 1},
                "average rent": {"$avg": "$rent"},
                "average deposit": {"$avg": "$deposit"}
            }
        }
    ])
for document in cursor:
    print(document)









    



{'count': 1, '_id': '서울시 동작구 신대방1동', 'average rent': 35.0, 'average deposit': 2000.0}
{'count': 1, '_id': '서울시 금천구 독산동', 'average rent': 0.0, 'average deposit': 10000.0}
{'count': 1, '_id': '서울시 구로구 구로3동', 'average rent': 40.0, 'average deposit': 300.0}
{'count': 1, '_id': '서울시 동작구 상도동', 'average rent': 29.0, 'average deposit': 500.0}
{'count': 6, '_id': '서울시 관악구 봉천동', 'average rent': 24.166666666666668, 'average deposit': 7466.666666666667}
{'count': 9, '_id': '서울시 관악구 신림동', 'average rent': 18.666666666666668, 'average deposit': 8000.0}



In [73]:

    
db = client.dobestan



In [74]:

    
db.collection_names()









    Out[74]:





['users', 'zigbang', 'world', 'watcha', 'restaurants']



In [75]:

    
restaurants = db.restaurants   #MONGO DB에서 제공하는 데이터



In [76]:

    
restaurants.find().count()









    Out[76]:





25359



In [77]:

    
cursor = restaurants.aggregate([
    {
       "$group": {
            "_id": "$borough",
        }     
    }
])
for document in cursor:
    print(document)









    



{'_id': 'Missing'}
{'_id': 'Staten Island'}
{'_id': 'Manhattan'}
{'_id': 'Brooklyn'}
{'_id': 'Queens'}
{'_id': 'Bronx'}



In [78]:

    
cursor = restaurants.aggregate([
    {
       "$group": {
            "_id": "$borough",
                "count": {"$sum": 1},
        }     
    }
])
for document in cursor:
    print(document)









    



{'_id': 'Missing', 'count': 51}
{'_id': 'Staten Island', 'count': 969}
{'_id': 'Manhattan', 'count': 10259}
{'_id': 'Brooklyn', 'count': 6086}
{'_id': 'Queens', 'count': 5656}
{'_id': 'Bronx', 'count': 2338}

관계형 DB를 몽고DB로 옮길 때 어떻게 옮기는 것이 가장 좋을까?

pandas로 country city df 옮겨보자



In [79]:

    
import pandas as pd
import pymysql

db = pymysql.connect(
    "db.fastcamp.us",
    "root",
    "dkstncks",
    "world",
    charset='utf8',
)



In [80]:

    
# db = MySQLdb.connect(
#     "192.168.0.199",
#     "root",
#     "rlarlvy",
#     "sakila",
#     charset='utf8',
# )
# => 이거 왜 안되는지 모르겠다.



In [81]:

    
country_df = pd.read_sql("SELECT * FROM Country;", db)
city_df = pd.read_sql("SELECT * FROM City;", db)



In [82]:

    
country_df.columns









    Out[82]:





Index(['Code', 'Name', 'Continent', 'Region', 'SurfaceArea', 'IndepYear',
       'Population', 'LifeExpectancy', 'GNP', 'GNPOld', 'LocalName',
       'GovernmentForm', 'HeadOfState', 'Capital', 'Code2'],
      dtype='object')



In [83]:

    
city_df.columns









    Out[83]:





Index(['ID', 'Name', 'CountryCode', 'District', 'Population'], dtype='object')



In [84]:

    
# List of countries.
[
    {
        "Name": "Republic of Korea",
        "cities": [ #List of cities
            {
                "Name": "Seoul",
                "Population": "100000000",
            },
            {
                "Name": "Busan",
            }
            
        ]
    }
]









    Out[84]:





[{'Name': 'Republic of Korea',
  'cities': [{'Name': 'Seoul', 'Population': '100000000'}, {'Name': 'Busan'}]}]



In [86]:

    
country_df.to_json()









    Out[86]:





'{"Code":{"0":"ABW","1":"AFG","2":"AGO","3":"AIA","4":"ALB","5":"AND","6":"ANT","7":"ARE","8":"ARG","9":"ARM","10":"ASM","11":"ATA","12":"ATF","13":"ATG","14":"AUS","15":"AUT","16":"AZE","17":"BDI","18":"BEL","19":"BEN","20":"BFA","21":"BGD","22":"BGR","23":"BHR","24":"BHS","25":"BIH","26":"BLR","27":"BLZ","28":"BMU","29":"BOL","30":"BRA","31":"BRB","32":"BRN","33":"BTN","34":"BVT","35":"BWA","36":"CAF","37":"CAN","38":"CCK","39":"CHE","40":"CHL","41":"CHN","42":"CIV","43":"CMR","44":"COD","45":"COG","46":"COK","47":"COL","48":"COM","49":"CPV","50":"CRI","51":"CUB","52":"CXR","53":"CYM","54":"CYP","55":"CZE","56":"DEU","57":"DJI","58":"DMA","59":"DNK","60":"DOM","61":"DZA","62":"ECU","63":"EGY","64":"ERI","65":"ESH","66":"ESP","67":"EST","68":"ETH","69":"FIN","70":"FJI","71":"FLK","72":"FRA","73":"FRO","74":"FSM","75":"GAB","76":"GBR","77":"GEO","78":"GHA","79":"GIB","80":"GIN","81":"GLP","82":"GMB","83":"GNB","84":"GNQ","85":"GRC","86":"GRD","87":"GRL","88":"GTM","89":"GUF","90":"GUM","91":"GUY","92":"HKG","93":"HMD","94":"HND","95":"HRV","96":"HTI","97":"HUN","98":"IDN","99":"IND","100":"IOT","101":"IRL","102":"IRN","103":"IRQ","104":"ISL","105":"ISR","106":"ITA","107":"JAM","108":"JOR","109":"JPN","110":"KAZ","111":"KEN","112":"KGZ","113":"KHM","114":"KIR","115":"KNA","116":"KOR","117":"KWT","118":"LAO","119":"LBN","120":"LBR","121":"LBY","122":"LCA","123":"LIE","124":"LKA","125":"LSO","126":"LTU","127":"LUX","128":"LVA","129":"MAC","130":"MAR","131":"MCO","132":"MDA","133":"MDG","134":"MDV","135":"MEX","136":"MHL","137":"MKD","138":"MLI","139":"MLT","140":"MMR","141":"MNG","142":"MNP","143":"MOZ","144":"MRT","145":"MSR","146":"MTQ","147":"MUS","148":"MWI","149":"MYS","150":"MYT","151":"NAM","152":"NCL","153":"NER","154":"NFK","155":"NGA","156":"NIC","157":"NIU","158":"NLD","159":"NOR","160":"NPL","161":"NRU","162":"NZL","163":"OMN","164":"PAK","165":"PAN","166":"PCN","167":"PER","168":"PHL","169":"PLW","170":"PNG","171":"POL","172":"PRI","173":"PRK","174":"PRT","175":"PRY","176":"PSE","177":"PYF","178":"QAT","179":"REU","180":"ROM","181":"RUS","182":"RWA","183":"SAU","184":"SDN","185":"SEN","186":"SGP","187":"SGS","188":"SHN","189":"SJM","190":"SLB","191":"SLE","192":"SLV","193":"SMR","194":"SOM","195":"SPM","196":"STP","197":"SUR","198":"SVK","199":"SVN","200":"SWE","201":"SWZ","202":"SYC","203":"SYR","204":"TCA","205":"TCD","206":"TGO","207":"THA","208":"TJK","209":"TKL","210":"TKM","211":"TMP","212":"TON","213":"TTO","214":"TUN","215":"TUR","216":"TUV","217":"TWN","218":"TZA","219":"UGA","220":"UKR","221":"UMI","222":"URY","223":"USA","224":"UZB","225":"VAT","226":"VCT","227":"VEN","228":"VGB","229":"VIR","230":"VNM","231":"VUT","232":"WLF","233":"WSM","234":"YEM","235":"YUG","236":"ZAF","237":"ZMB","238":"ZWE"},"Name":{"0":"Aruba","1":"Afghanistan","2":"Angola","3":"Anguilla","4":"Albania","5":"Andorra","6":"Netherlands Antilles","7":"United Arab Emirates","8":"Argentina","9":"Armenia","10":"American Samoa","11":"Antarctica","12":"French Southern territories","13":"Antigua and Barbuda","14":"Australia","15":"Austria","16":"Azerbaijan","17":"Burundi","18":"Belgium","19":"Benin","20":"Burkina Faso","21":"Bangladesh","22":"Bulgaria","23":"Bahrain","24":"Bahamas","25":"Bosnia and Herzegovina","26":"Belarus","27":"Belize","28":"Bermuda","29":"Bolivia","30":"Brazil","31":"Barbados","32":"Brunei","33":"Bhutan","34":"Bouvet Island","35":"Botswana","36":"Central African Republic","37":"Canada","38":"Cocos (Keeling) Islands","39":"Switzerland","40":"Chile","41":"China","42":"C\\u00f4te d\\u2019Ivoire","43":"Cameroon","44":"Congo, The Democratic Republic of the","45":"Congo","46":"Cook Islands","47":"Colombia","48":"Comoros","49":"Cape Verde","50":"Costa Rica","51":"Cuba","52":"Christmas Island","53":"Cayman Islands","54":"Cyprus","55":"Czech Republic","56":"Germany","57":"Djibouti","58":"Dominica","59":"Denmark","60":"Dominican Republic","61":"Algeria","62":"Ecuador","63":"Egypt","64":"Eritrea","65":"Western Sahara","66":"Spain","67":"Estonia","68":"Ethiopia","69":"Finland","70":"Fiji Islands","71":"Falkland Islands","72":"France","73":"Faroe Islands","74":"Micronesia, Federated States of","75":"Gabon","76":"United Kingdom","77":"Georgia","78":"Ghana","79":"Gibraltar","80":"Guinea","81":"Guadeloupe","82":"Gambia","83":"Guinea-Bissau","84":"Equatorial Guinea","85":"Greece","86":"Grenada","87":"Greenland","88":"Guatemala","89":"French Guiana","90":"Guam","91":"Guyana","92":"Hong Kong","93":"Heard Island and McDonald Islands","94":"Honduras","95":"Croatia","96":"Haiti","97":"Hungary","98":"Indonesia","99":"India","100":"British Indian Ocean Territory","101":"Ireland","102":"Iran","103":"Iraq","104":"Iceland","105":"Israel","106":"Italy","107":"Jamaica","108":"Jordan","109":"Japan","110":"Kazakstan","111":"Kenya","112":"Kyrgyzstan","113":"Cambodia","114":"Kiribati","115":"Saint Kitts and Nevis","116":"South Korea","117":"Kuwait","118":"Laos","119":"Lebanon","120":"Liberia","121":"Libyan Arab Jamahiriya","122":"Saint Lucia","123":"Liechtenstein","124":"Sri Lanka","125":"Lesotho","126":"Lithuania","127":"Luxembourg","128":"Latvia","129":"Macao","130":"Morocco","131":"Monaco","132":"Moldova","133":"Madagascar","134":"Maldives","135":"Mexico","136":"Marshall Islands","137":"Macedonia","138":"Mali","139":"Malta","140":"Myanmar","141":"Mongolia","142":"Northern Mariana Islands","143":"Mozambique","144":"Mauritania","145":"Montserrat","146":"Martinique","147":"Mauritius","148":"Malawi","149":"Malaysia","150":"Mayotte","151":"Namibia","152":"New Caledonia","153":"Niger","154":"Norfolk Island","155":"Nigeria","156":"Nicaragua","157":"Niue","158":"Netherlands","159":"Norway","160":"Nepal","161":"Nauru","162":"New Zealand","163":"Oman","164":"Pakistan","165":"Panama","166":"Pitcairn","167":"Peru","168":"Philippines","169":"Palau","170":"Papua New Guinea","171":"Poland","172":"Puerto Rico","173":"North Korea","174":"Portugal","175":"Paraguay","176":"Palestine","177":"French Polynesia","178":"Qatar","179":"R\\u00e9union","180":"Romania","181":"Russian Federation","182":"Rwanda","183":"Saudi Arabia","184":"Sudan","185":"Senegal","186":"Singapore","187":"South Georgia and the South Sandwich Islands","188":"Saint Helena","189":"Svalbard and Jan Mayen","190":"Solomon Islands","191":"Sierra Leone","192":"El Salvador","193":"San Marino","194":"Somalia","195":"Saint Pierre and Miquelon","196":"Sao Tome and Principe","197":"Suriname","198":"Slovakia","199":"Slovenia","200":"Sweden","201":"Swaziland","202":"Seychelles","203":"Syria","204":"Turks and Caicos Islands","205":"Chad","206":"Togo","207":"Thailand","208":"Tajikistan","209":"Tokelau","210":"Turkmenistan","211":"East Timor","212":"Tonga","213":"Trinidad and Tobago","214":"Tunisia","215":"Turkey","216":"Tuvalu","217":"Taiwan","218":"Tanzania","219":"Uganda","220":"Ukraine","221":"United States Minor Outlying Islands","222":"Uruguay","223":"United States","224":"Uzbekistan","225":"Holy See (Vatican City State)","226":"Saint Vincent and the Grenadines","227":"Venezuela","228":"Virgin Islands, British","229":"Virgin Islands, U.S.","230":"Vietnam","231":"Vanuatu","232":"Wallis and Futuna","233":"Samoa","234":"Yemen","235":"Yugoslavia","236":"South Africa","237":"Zambia","238":"Zimbabwe"},"Continent":{"0":"North America","1":"Asia","2":"Africa","3":"North America","4":"Europe","5":"Europe","6":"North America","7":"Asia","8":"South America","9":"Asia","10":"Oceania","11":"Antarctica","12":"Antarctica","13":"North America","14":"Oceania","15":"Europe","16":"Asia","17":"Africa","18":"Europe","19":"Africa","20":"Africa","21":"Asia","22":"Europe","23":"Asia","24":"North America","25":"Europe","26":"Europe","27":"North America","28":"North America","29":"South America","30":"South America","31":"North America","32":"Asia","33":"Asia","34":"Antarctica","35":"Africa","36":"Africa","37":"North America","38":"Oceania","39":"Europe","40":"South America","41":"Asia","42":"Africa","43":"Africa","44":"Africa","45":"Africa","46":"Oceania","47":"South America","48":"Africa","49":"Africa","50":"North America","51":"North America","52":"Oceania","53":"North America","54":"Asia","55":"Europe","56":"Europe","57":"Africa","58":"North America","59":"Europe","60":"North America","61":"Africa","62":"South America","63":"Africa","64":"Africa","65":"Africa","66":"Europe","67":"Europe","68":"Africa","69":"Europe","70":"Oceania","71":"South 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In [85]:

    
country_df.to_dict()









    Out[85]:





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  59: 3315.0,
  60: 587.0,
  61: 35.0,
  62: 594.0,
  63: 608.0,
  64: 652.0,
  65: 2453.0,
  66: 653.0,
  67: 3791.0,
  68: 756.0,
  69: 3236.0,
  70: 764.0,
  71: 763.0,
  72: 2974.0,
  73: 901.0,
  74: 2689.0,
  75: 902.0,
  76: 456.0,
  77: 905.0,
  78: 910.0,
  79: 915.0,
  80: 926.0,
  81: 919.0,
  82: 904.0,
  83: 927.0,
  84: 2972.0,
  85: 2401.0,
  86: 916.0,
  87: 917.0,
  88: 922.0,
  89: 3014.0,
  90: 921.0,
  91: 928.0,
  92: 937.0,
  93: nan,
  94: 933.0,
  95: 2409.0,
  96: 929.0,
  97: 3483.0,
  98: 939.0,
  99: 1109.0,
  100: nan,
  101: 1447.0,
  102: 1380.0,
  103: 1365.0,
  104: 1449.0,
  105: 1450.0,
  106: 1464.0,
  107: 1530.0,
  108: 1786.0,
  109: 1532.0,
  110: 1864.0,
  111: 1881.0,
  112: 2253.0,
  113: 1800.0,
  114: 2256.0,
  115: 3064.0,
  116: 2331.0,
  117: 2429.0,
  118: 2432.0,
  119: 2438.0,
  120: 2440.0,
  121: 2441.0,
  122: 3065.0,
  123: 2446.0,
  124: 3217.0,
  125: 2437.0,
  126: 2447.0,
  127: 2452.0,
  128: 2434.0,
  129: 2454.0,
  130: 2486.0,
  131: 2695.0,
  132: 2690.0,
  133: 2455.0,
  134: 2463.0,
  135: 2515.0,
  136: 2507.0,
  137: 2460.0,
  138: 2482.0,
  139: 2484.0,
  140: 2710.0,
  141: 2696.0,
  142: 2913.0,
  143: 2698.0,
  144: 2509.0,
  145: 2697.0,
  146: 2508.0,
  147: 2511.0,
  148: 2462.0,
  149: 2464.0,
  150: 2514.0,
  151: 2726.0,
  152: 3493.0,
  153: 2738.0,
  154: 2806.0,
  155: 2754.0,
  156: 2734.0,
  157: 2805.0,
  158: 5.0,
  159: 2807.0,
  160: 2729.0,
  161: 2728.0,
  162: 3499.0,
  163: 2821.0,
  164: 2831.0,
  165: 2882.0,
  166: 2912.0,
  167: 2890.0,
  168: 766.0,
  169: 2881.0,
  170: 2884.0,
  171: 2928.0,
  172: 2919.0,
  173: 2318.0,
  174: 2914.0,
  175: 2885.0,
  176: 4074.0,
  177: 3016.0,
  178: 2973.0,
  179: 3017.0,
  180: 3018.0,
  181: 3580.0,
  182: 3047.0,
  183: 3173.0,
  184: 3225.0,
  185: 3198.0,
  186: 3208.0,
  187: nan,
  188: 3063.0,
  189: 938.0,
  190: 3161.0,
  191: 3207.0,
  192: 645.0,
  193: 3171.0,
  194: 3214.0,
  195: 3067.0,
  196: 3172.0,
  197: 3243.0,
  198: 3209.0,
  199: 3212.0,
  200: 3048.0,
  201: 3244.0,
  202: 3206.0,
  203: 3250.0,
  204: 3423.0,
  205: 3337.0,
  206: 3332.0,
  207: 3320.0,
  208: 3261.0,
  209: 3333.0,
  210: 3419.0,
  211: 1522.0,
  212: 3334.0,
  213: 3336.0,
  214: 3349.0,
  215: 3358.0,
  216: 3424.0,
  217: 3263.0,
  218: 3306.0,
  219: 3425.0,
  220: 3426.0,
  221: nan,
  222: 3492.0,
  223: 3813.0,
  224: 3503.0,
  225: 3538.0,
  226: 3066.0,
  227: 3539.0,
  228: 537.0,
  229: 4067.0,
  230: 3770.0,
  231: 3537.0,
  232: 3536.0,
  233: 3169.0,
  234: 1780.0,
  235: 1792.0,
  236: 716.0,
  237: 3162.0,
  238: 4068.0},
 'Code': {0: 'ABW',
  1: 'AFG',
  2: 'AGO',
  3: 'AIA',
  4: 'ALB',
  5: 'AND',
  6: 'ANT',
  7: 'ARE',
  8: 'ARG',
  9: 'ARM',
  10: 'ASM',
  11: 'ATA',
  12: 'ATF',
  13: 'ATG',
  14: 'AUS',
  15: 'AUT',
  16: 'AZE',
  17: 'BDI',
  18: 'BEL',
  19: 'BEN',
  20: 'BFA',
  21: 'BGD',
  22: 'BGR',
  23: 'BHR',
  24: 'BHS',
  25: 'BIH',
  26: 'BLR',
  27: 'BLZ',
  28: 'BMU',
  29: 'BOL',
  30: 'BRA',
  31: 'BRB',
  32: 'BRN',
  33: 'BTN',
  34: 'BVT',
  35: 'BWA',
  36: 'CAF',
  37: 'CAN',
  38: 'CCK',
  39: 'CHE',
  40: 'CHL',
  41: 'CHN',
  42: 'CIV',
  43: 'CMR',
  44: 'COD',
  45: 'COG',
  46: 'COK',
  47: 'COL',
  48: 'COM',
  49: 'CPV',
  50: 'CRI',
  51: 'CUB',
  52: 'CXR',
  53: 'CYM',
  54: 'CYP',
  55: 'CZE',
  56: 'DEU',
  57: 'DJI',
  58: 'DMA',
  59: 'DNK',
  60: 'DOM',
  61: 'DZA',
  62: 'ECU',
  63: 'EGY',
  64: 'ERI',
  65: 'ESH',
  66: 'ESP',
  67: 'EST',
  68: 'ETH',
  69: 'FIN',
  70: 'FJI',
  71: 'FLK',
  72: 'FRA',
  73: 'FRO',
  74: 'FSM',
  75: 'GAB',
  76: 'GBR',
  77: 'GEO',
  78: 'GHA',
  79: 'GIB',
  80: 'GIN',
  81: 'GLP',
  82: 'GMB',
  83: 'GNB',
  84: 'GNQ',
  85: 'GRC',
  86: 'GRD',
  87: 'GRL',
  88: 'GTM',
  89: 'GUF',
  90: 'GUM',
  91: 'GUY',
  92: 'HKG',
  93: 'HMD',
  94: 'HND',
  95: 'HRV',
  96: 'HTI',
  97: 'HUN',
  98: 'IDN',
  99: 'IND',
  100: 'IOT',
  101: 'IRL',
  102: 'IRN',
  103: 'IRQ',
  104: 'ISL',
  105: 'ISR',
  106: 'ITA',
  107: 'JAM',
  108: 'JOR',
  109: 'JPN',
  110: 'KAZ',
  111: 'KEN',
  112: 'KGZ',
  113: 'KHM',
  114: 'KIR',
  115: 'KNA',
  116: 'KOR',
  117: 'KWT',
  118: 'LAO',
  119: 'LBN',
  120: 'LBR',
  121: 'LBY',
  122: 'LCA',
  123: 'LIE',
  124: 'LKA',
  125: 'LSO',
  126: 'LTU',
  127: 'LUX',
  128: 'LVA',
  129: 'MAC',
  130: 'MAR',
  131: 'MCO',
  132: 'MDA',
  133: 'MDG',
  134: 'MDV',
  135: 'MEX',
  136: 'MHL',
  137: 'MKD',
  138: 'MLI',
  139: 'MLT',
  140: 'MMR',
  141: 'MNG',
  142: 'MNP',
  143: 'MOZ',
  144: 'MRT',
  145: 'MSR',
  146: 'MTQ',
  147: 'MUS',
  148: 'MWI',
  149: 'MYS',
  150: 'MYT',
  151: 'NAM',
  152: 'NCL',
  153: 'NER',
  154: 'NFK',
  155: 'NGA',
  156: 'NIC',
  157: 'NIU',
  158: 'NLD',
  159: 'NOR',
  160: 'NPL',
  161: 'NRU',
  162: 'NZL',
  163: 'OMN',
  164: 'PAK',
  165: 'PAN',
  166: 'PCN',
  167: 'PER',
  168: 'PHL',
  169: 'PLW',
  170: 'PNG',
  171: 'POL',
  172: 'PRI',
  173: 'PRK',
  174: 'PRT',
  175: 'PRY',
  176: 'PSE',
  177: 'PYF',
  178: 'QAT',
  179: 'REU',
  180: 'ROM',
  181: 'RUS',
  182: 'RWA',
  183: 'SAU',
  184: 'SDN',
  185: 'SEN',
  186: 'SGP',
  187: 'SGS',
  188: 'SHN',
  189: 'SJM',
  190: 'SLB',
  191: 'SLE',
  192: 'SLV',
  193: 'SMR',
  194: 'SOM',
  195: 'SPM',
  196: 'STP',
  197: 'SUR',
  198: 'SVK',
  199: 'SVN',
  200: 'SWE',
  201: 'SWZ',
  202: 'SYC',
  203: 'SYR',
  204: 'TCA',
  205: 'TCD',
  206: 'TGO',
  207: 'THA',
  208: 'TJK',
  209: 'TKL',
  210: 'TKM',
  211: 'TMP',
  212: 'TON',
  213: 'TTO',
  214: 'TUN',
  215: 'TUR',
  216: 'TUV',
  217: 'TWN',
  218: 'TZA',
  219: 'UGA',
  220: 'UKR',
  221: 'UMI',
  222: 'URY',
  223: 'USA',
  224: 'UZB',
  225: 'VAT',
  226: 'VCT',
  227: 'VEN',
  228: 'VGB',
  229: 'VIR',
  230: 'VNM',
  231: 'VUT',
  232: 'WLF',
  233: 'WSM',
  234: 'YEM',
  235: 'YUG',
  236: 'ZAF',
  237: 'ZMB',
  238: 'ZWE'},
 'Code2': {0: 'AW',
  1: 'AF',
  2: 'AO',
  3: 'AI',
  4: 'AL',
  5: 'AD',
  6: 'AN',
  7: 'AE',
  8: 'AR',
  9: 'AM',
  10: 'AS',
  11: 'AQ',
  12: 'TF',
  13: 'AG',
  14: 'AU',
  15: 'AT',
  16: 'AZ',
  17: 'BI',
  18: 'BE',
  19: 'BJ',
  20: 'BF',
  21: 'BD',
  22: 'BG',
  23: 'BH',
  24: 'BS',
  25: 'BA',
  26: 'BY',
  27: 'BZ',
  28: 'BM',
  29: 'BO',
  30: 'BR',
  31: 'BB',
  32: 'BN',
  33: 'BT',
  34: 'BV',
  35: 'BW',
  36: 'CF',
  37: 'CA',
  38: 'CC',
  39: 'CH',
  40: 'CL',
  41: 'CN',
  42: 'CI',
  43: 'CM',
  44: 'CD',
  45: 'CG',
  46: 'CK',
  47: 'CO',
  48: 'KM',
  49: 'CV',
  50: 'CR',
  51: 'CU',
  52: 'CX',
  53: 'KY',
  54: 'CY',
  55: 'CZ',
  56: 'DE',
  57: 'DJ',
  58: 'DM',
  59: 'DK',
  60: 'DO',
  61: 'DZ',
  62: 'EC',
  63: 'EG',
  64: 'ER',
  65: 'EH',
  66: 'ES',
  67: 'EE',
  68: 'ET',
  69: 'FI',
  70: 'FJ',
  71: 'FK',
  72: 'FR',
  73: 'FO',
  74: 'FM',
  75: 'GA',
  76: 'GB',
  77: 'GE',
  78: 'GH',
  79: 'GI',
  80: 'GN',
  81: 'GP',
  82: 'GM',
  83: 'GW',
  84: 'GQ',
  85: 'GR',
  86: 'GD',
  87: 'GL',
  88: 'GT',
  89: 'GF',
  90: 'GU',
  91: 'GY',
  92: 'HK',
  93: 'HM',
  94: 'HN',
  95: 'HR',
  96: 'HT',
  97: 'HU',
  98: 'ID',
  99: 'IN',
  100: 'IO',
  101: 'IE',
  102: 'IR',
  103: 'IQ',
  104: 'IS',
  105: 'IL',
  106: 'IT',
  107: 'JM',
  108: 'JO',
  109: 'JP',
  110: 'KZ',
  111: 'KE',
  112: 'KG',
  113: 'KH',
  114: 'KI',
  115: 'KN',
  116: 'KR',
  117: 'KW',
  118: 'LA',
  119: 'LB',
  120: 'LR',
  121: 'LY',
  122: 'LC',
  123: 'LI',
  124: 'LK',
  125: 'LS',
  126: 'LT',
  127: 'LU',
  128: 'LV',
  129: 'MO',
  130: 'MA',
  131: 'MC',
  132: 'MD',
  133: 'MG',
  134: 'MV',
  135: 'MX',
  136: 'MH',
  137: 'MK',
  138: 'ML',
  139: 'MT',
  140: 'MM',
  141: 'MN',
  142: 'MP',
  143: 'MZ',
  144: 'MR',
  145: 'MS',
  146: 'MQ',
  147: 'MU',
  148: 'MW',
  149: 'MY',
  150: 'YT',
  151: 'NA',
  152: 'NC',
  153: 'NE',
  154: 'NF',
  155: 'NG',
  156: 'NI',
  157: 'NU',
  158: 'NL',
  159: 'NO',
  160: 'NP',
  161: 'NR',
  162: 'NZ',
  163: 'OM',
  164: 'PK',
  165: 'PA',
  166: 'PN',
  167: 'PE',
  168: 'PH',
  169: 'PW',
  170: 'PG',
  171: 'PL',
  172: 'PR',
  173: 'KP',
  174: 'PT',
  175: 'PY',
  176: 'PS',
  177: 'PF',
  178: 'QA',
  179: 'RE',
  180: 'RO',
  181: 'RU',
  182: 'RW',
  183: 'SA',
  184: 'SD',
  185: 'SN',
  186: 'SG',
  187: 'GS',
  188: 'SH',
  189: 'SJ',
  190: 'SB',
  191: 'SL',
  192: 'SV',
  193: 'SM',
  194: 'SO',
  195: 'PM',
  196: 'ST',
  197: 'SR',
  198: 'SK',
  199: 'SI',
  200: 'SE',
  201: 'SZ',
  202: 'SC',
  203: 'SY',
  204: 'TC',
  205: 'TD',
  206: 'TG',
  207: 'TH',
  208: 'TJ',
  209: 'TK',
  210: 'TM',
  211: 'TP',
  212: 'TO',
  213: 'TT',
  214: 'TN',
  215: 'TR',
  216: 'TV',
  217: 'TW',
  218: 'TZ',
  219: 'UG',
  220: 'UA',
  221: 'UM',
  222: 'UY',
  223: 'US',
  224: 'UZ',
  225: 'VA',
  226: 'VC',
  227: 'VE',
  228: 'VG',
  229: 'VI',
  230: 'VN',
  231: 'VU',
  232: 'WF',
  233: 'WS',
  234: 'YE',
  235: 'YU',
  236: 'ZA',
  237: 'ZM',
  238: 'ZW'},
 'Continent': {0: 'North America',
  1: 'Asia',
  2: 'Africa',
  3: 'North America',
  4: 'Europe',
  5: 'Europe',
  6: 'North America',
  7: 'Asia',
  8: 'South America',
  9: 'Asia',
  10: 'Oceania',
  11: 'Antarctica',
  12: 'Antarctica',
  13: 'North America',
  14: 'Oceania',
  15: 'Europe',
  16: 'Asia',
  17: 'Africa',
  18: 'Europe',
  19: 'Africa',
  20: 'Africa',
  21: 'Asia',
  22: 'Europe',
  23: 'Asia',
  24: 'North America',
  25: 'Europe',
  26: 'Europe',
  27: 'North America',
  28: 'North America',
  29: 'South America',
  30: 'South America',
  31: 'North America',
  32: 'Asia',
  33: 'Asia',
  34: 'Antarctica',
  35: 'Africa',
  36: 'Africa',
  37: 'North America',
  38: 'Oceania',
  39: 'Europe',
  40: 'South America',
  41: 'Asia',
  42: 'Africa',
  43: 'Africa',
  44: 'Africa',
  45: 'Africa',
  46: 'Oceania',
  47: 'South America',
  48: 'Africa',
  49: 'Africa',
  50: 'North America',
  51: 'North America',
  52: 'Oceania',
  53: 'North America',
  54: 'Asia',
  55: 'Europe',
  56: 'Europe',
  57: 'Africa',
  58: 'North America',
  59: 'Europe',
  60: 'North America',
  61: 'Africa',
  62: 'South America',
  63: 'Africa',
  64: 'Africa',
  65: 'Africa',
  66: 'Europe',
  67: 'Europe',
  68: 'Africa',
  69: 'Europe',
  70: 'Oceania',
  71: 'South America',
  72: 'Europe',
  73: 'Europe',
  74: 'Oceania',
  75: 'Africa',
  76: 'Europe',
  77: 'Asia',
  78: 'Africa',
  79: 'Europe',
  80: 'Africa',
  81: 'North America',
  82: 'Africa',
  83: 'Africa',
  84: 'Africa',
  85: 'Europe',
  86: 'North America',
  87: 'North America',
  88: 'North America',
  89: 'South America',
  90: 'Oceania',
  91: 'South America',
  92: 'Asia',
  93: 'Antarctica',
  94: 'North America',
  95: 'Europe',
  96: 'North America',
  97: 'Europe',
  98: 'Asia',
  99: 'Asia',
  100: 'Africa',
  101: 'Europe',
  102: 'Asia',
  103: 'Asia',
  104: 'Europe',
  105: 'Asia',
  106: 'Europe',
  107: 'North America',
  108: 'Asia',
  109: 'Asia',
  110: 'Asia',
  111: 'Africa',
  112: 'Asia',
  113: 'Asia',
  114: 'Oceania',
  115: 'North America',
  116: 'Asia',
  117: 'Asia',
  118: 'Asia',
  119: 'Asia',
  120: 'Africa',
  121: 'Africa',
  122: 'North America',
  123: 'Europe',
  124: 'Asia',
  125: 'Africa',
  126: 'Europe',
  127: 'Europe',
  128: 'Europe',
  129: 'Asia',
  130: 'Africa',
  131: 'Europe',
  132: 'Europe',
  133: 'Africa',
  134: 'Asia',
  135: 'North America',
  136: 'Oceania',
  137: 'Europe',
  138: 'Africa',
  139: 'Europe',
  140: 'Asia',
  141: 'Asia',
  142: 'Oceania',
  143: 'Africa',
  144: 'Africa',
  145: 'North America',
  146: 'North America',
  147: 'Africa',
  148: 'Africa',
  149: 'Asia',
  150: 'Africa',
  151: 'Africa',
  152: 'Oceania',
  153: 'Africa',
  154: 'Oceania',
  155: 'Africa',
  156: 'North America',
  157: 'Oceania',
  158: 'Europe',
  159: 'Europe',
  160: 'Asia',
  161: 'Oceania',
  162: 'Oceania',
  163: 'Asia',
  164: 'Asia',
  165: 'North America',
  166: 'Oceania',
  167: 'South America',
  168: 'Asia',
  169: 'Oceania',
  170: 'Oceania',
  171: 'Europe',
  172: 'North America',
  173: 'Asia',
  174: 'Europe',
  175: 'South America',
  176: 'Asia',
  177: 'Oceania',
  178: 'Asia',
  179: 'Africa',
  180: 'Europe',
  181: 'Europe',
  182: 'Africa',
  183: 'Asia',
  184: 'Africa',
  185: 'Africa',
  186: 'Asia',
  187: 'Antarctica',
  188: 'Africa',
  189: 'Europe',
  190: 'Oceania',
  191: 'Africa',
  192: 'North America',
  193: 'Europe',
  194: 'Africa',
  195: 'North America',
  196: 'Africa',
  197: 'South America',
  198: 'Europe',
  199: 'Europe',
  200: 'Europe',
  201: 'Africa',
  202: 'Africa',
  203: 'Asia',
  204: 'North America',
  205: 'Africa',
  206: 'Africa',
  207: 'Asia',
  208: 'Asia',
  209: 'Oceania',
  210: 'Asia',
  211: 'Asia',
  212: 'Oceania',
  213: 'North America',
  214: 'Africa',
  215: 'Asia',
  216: 'Oceania',
  217: 'Asia',
  218: 'Africa',
  219: 'Africa',
  220: 'Europe',
  221: 'Oceania',
  222: 'South America',
  223: 'North America',
  224: 'Asia',
  225: 'Europe',
  226: 'North America',
  227: 'South America',
  228: 'North America',
  229: 'North America',
  230: 'Asia',
  231: 'Oceania',
  232: 'Oceania',
  233: 'Oceania',
  234: 'Asia',
  235: 'Europe',
  236: 'Africa',
  237: 'Africa',
  238: 'Africa'},
 'GNP': {0: 828.0,
  1: 5976.0,
  2: 6648.0,
  3: 63.200000000000003,
  4: 3205.0,
  5: 1630.0,
  6: 1941.0,
  7: 37966.0,
  8: 340238.0,
  9: 1813.0,
  10: 334.0,
  11: 0.0,
  12: 0.0,
  13: 612.0,
  14: 351182.0,
  15: 211860.0,
  16: 4127.0,
  17: 903.0,
  18: 249704.0,
  19: 2357.0,
  20: 2425.0,
  21: 32852.0,
  22: 12178.0,
  23: 6366.0,
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  118: 'Khamtay Siphandone',
  119: 'Émile Lahoud',
  120: 'Charles Taylor',
  121: 'Muammar al-Qadhafi',
  122: 'Elisabeth II',
  123: 'Hans-Adam II',
  124: 'Chandrika Kumaratunga',
  125: 'Letsie III',
  126: 'Valdas Adamkus',
  127: 'Henri',
  128: 'Vaira Vike-Freiberga',
  129: 'Jiang Zemin',
  130: 'Mohammed VI',
  131: 'Rainier III',
  132: 'Vladimir Voronin',
  133: 'Didier Ratsiraka',
  134: 'Maumoon Abdul Gayoom',
  135: 'Vicente Fox Quesada',
  136: 'Kessai Note',
  137: 'Boris Trajkovski',
  138: 'Alpha Oumar Konaré',
  139: 'Guido de Marco',
  140: 'kenraali Than Shwe',
  141: 'Natsagiin Bagabandi',
  142: 'George W. Bush',
  143: 'Joaquím A. Chissano',
  144: 'Maaouiya Ould Sid´Ahmad Taya',
  145: 'Elisabeth II',
  146: 'Jacques Chirac',
  147: 'Cassam Uteem',
  148: 'Bakili Muluzi',
  149: 'Salahuddin Abdul Aziz Shah Alhaj',
  150: 'Jacques Chirac',
  151: 'Sam Nujoma',
  152: 'Jacques Chirac',
  153: 'Mamadou Tandja',
  154: 'Elisabeth II',
  155: 'Olusegun Obasanjo',
  156: 'Arnoldo Alemán Lacayo',
  157: 'Elisabeth II',
  158: 'Beatrix',
  159: 'Harald V',
  160: 'Gyanendra Bir Bikram',
  161: 'Bernard Dowiyogo',
  162: 'Elisabeth II',
  163: 'Qabus ibn Sa´id',
  164: 'Mohammad Rafiq Tarar',
  165: 'Mireya Elisa Moscoso Rodríguez',
  166: 'Elisabeth II',
  167: 'Valentin Paniagua Corazao',
  168: 'Gloria Macapagal-Arroyo',
  169: 'Kuniwo Nakamura',
  170: 'Elisabeth II',
  171: 'Aleksander Kwasniewski',
  172: 'George W. Bush',
  173: 'Kim Jong-il',
  174: 'Jorge Sampãio',
  175: 'Luis Ángel González Macchi',
  176: 'Yasser (Yasir) Arafat',
  177: 'Jacques Chirac',
  178: 'Hamad ibn Khalifa al-Thani',
  179: 'Jacques Chirac',
  180: 'Ion Iliescu',
  181: 'Vladimir Putin',
  182: 'Paul Kagame',
  183: 'Fahd ibn Abdul-Aziz al-Sa´ud',
  184: 'Omar Hassan Ahmad al-Bashir',
  185: 'Abdoulaye Wade',
  186: 'Sellapan Rama Nathan',
  187: 'Elisabeth II',
  188: 'Elisabeth II',
  189: 'Harald V',
  190: 'Elisabeth II',
  191: 'Ahmed Tejan Kabbah',
  192: 'Francisco Guillermo Flores Pérez',
  193: None,
  194: 'Abdiqassim Salad Hassan',
  195: 'Jacques Chirac',
  196: 'Miguel Trovoada',
  197: 'Ronald Venetiaan',
  198: 'Rudolf Schuster',
  199: 'Milan Kucan',
  200: 'Carl XVI Gustaf',
  201: 'Mswati III',
  202: 'France-Albert René',
  203: 'Bashar al-Assad',
  204: 'Elisabeth II',
  205: 'Idriss Déby',
  206: 'Gnassingbé Eyadéma',
  207: 'Bhumibol Adulyadej',
  208: 'Emomali Rahmonov',
  209: 'Elisabeth II',
  210: 'Saparmurad Nijazov',
  211: 'José Alexandre Gusmão',
  212: "Taufa'ahau Tupou IV",
  213: 'Arthur N. R. Robinson',
  214: 'Zine al-Abidine Ben Ali',
  215: 'Ahmet Necdet Sezer',
  216: 'Elisabeth II',
  217: 'Chen Shui-bian',
  218: 'Benjamin William Mkapa',
  219: 'Yoweri Museveni',
  220: 'Leonid Kutšma',
  221: 'George W. Bush',
  222: 'Jorge Batlle Ibáñez',
  223: 'George W. Bush',
  224: 'Islam Karimov',
  225: 'Johannes Paavali II',
  226: 'Elisabeth II',
  227: 'Hugo Chávez Frías',
  228: 'Elisabeth II',
  229: 'George W. Bush',
  230: 'Trân Duc Luong',
  231: 'John Bani',
  232: 'Jacques Chirac',
  233: 'Malietoa Tanumafili II',
  234: 'Ali Abdallah Salih',
  235: 'Vojislav Koštunica',
  236: 'Thabo Mbeki',
  237: 'Frederick Chiluba',
  238: 'Robert G. Mugabe'},
 'IndepYear': {0: nan,
  1: 1919.0,
  2: 1975.0,
  3: nan,
  4: 1912.0,
  5: 1278.0,
  6: nan,
  7: 1971.0,
  8: 1816.0,
  9: 1991.0,
  10: nan,
  11: nan,
  12: nan,
  13: 1981.0,
  14: 1901.0,
  15: 1918.0,
  16: 1991.0,
  17: 1962.0,
  18: 1830.0,
  19: 1960.0,
  20: 1960.0,
  21: 1971.0,
  22: 1908.0,
  23: 1971.0,
  24: 1973.0,
  25: 1992.0,
  26: 1991.0,
  27: 1981.0,
  28: nan,
  29: 1825.0,
  30: 1822.0,
  31: 1966.0,
  32: 1984.0,
  33: 1910.0,
  34: nan,
  35: 1966.0,
  36: 1960.0,
  37: 1867.0,
  38: nan,
  39: 1499.0,
  40: 1810.0,
  41: -1523.0,
  42: 1960.0,
  43: 1960.0,
  44: 1960.0,
  45: 1960.0,
  46: nan,
  47: 1810.0,
  48: 1975.0,
  49: 1975.0,
  50: 1821.0,
  51: 1902.0,
  52: nan,
  53: nan,
  54: 1960.0,
  55: 1993.0,
  56: 1955.0,
  57: 1977.0,
  58: 1978.0,
  59: 800.0,
  60: 1844.0,
  61: 1962.0,
  62: 1822.0,
  63: 1922.0,
  64: 1993.0,
  65: nan,
  66: 1492.0,
  67: 1991.0,
  68: -1000.0,
  69: 1917.0,
  70: 1970.0,
  71: nan,
  72: 843.0,
  73: nan,
  74: 1990.0,
  75: 1960.0,
  76: 1066.0,
  77: 1991.0,
  78: 1957.0,
  79: nan,
  80: 1958.0,
  81: nan,
  82: 1965.0,
  83: 1974.0,
  84: 1968.0,
  85: 1830.0,
  86: 1974.0,
  87: nan,
  88: 1821.0,
  89: nan,
  90: nan,
  91: 1966.0,
  92: nan,
  93: nan,
  94: 1838.0,
  95: 1991.0,
  96: 1804.0,
  97: 1918.0,
  98: 1945.0,
  99: 1947.0,
  100: nan,
  101: 1921.0,
  102: 1906.0,
  103: 1932.0,
  104: 1944.0,
  105: 1948.0,
  106: 1861.0,
  107: 1962.0,
  108: 1946.0,
  109: -660.0,
  110: 1991.0,
  111: 1963.0,
  112: 1991.0,
  113: 1953.0,
  114: 1979.0,
  115: 1983.0,
  116: 1948.0,
  117: 1961.0,
  118: 1953.0,
  119: 1941.0,
  120: 1847.0,
  121: 1951.0,
  122: 1979.0,
  123: 1806.0,
  124: 1948.0,
  125: 1966.0,
  126: 1991.0,
  127: 1867.0,
  128: 1991.0,
  129: nan,
  130: 1956.0,
  131: 1861.0,
  132: 1991.0,
  133: 1960.0,
  134: 1965.0,
  135: 1810.0,
  136: 1990.0,
  137: 1991.0,
  138: 1960.0,
  139: 1964.0,
  140: 1948.0,
  141: 1921.0,
  142: nan,
  143: 1975.0,
  144: 1960.0,
  145: nan,
  146: nan,
  147: 1968.0,
  148: 1964.0,
  149: 1957.0,
  150: nan,
  151: 1990.0,
  152: nan,
  153: 1960.0,
  154: nan,
  155: 1960.0,
  156: 1838.0,
  157: nan,
  158: 1581.0,
  159: 1905.0,
  160: 1769.0,
  161: 1968.0,
  162: 1907.0,
  163: 1951.0,
  164: 1947.0,
  165: 1903.0,
  166: nan,
  167: 1821.0,
  168: 1946.0,
  169: 1994.0,
  170: 1975.0,
  171: 1918.0,
  172: nan,
  173: 1948.0,
  174: 1143.0,
  175: 1811.0,
  176: nan,
  177: nan,
  178: 1971.0,
  179: nan,
  180: 1878.0,
  181: 1991.0,
  182: 1962.0,
  183: 1932.0,
  184: 1956.0,
  185: 1960.0,
  186: 1965.0,
  187: nan,
  188: nan,
  189: nan,
  190: 1978.0,
  191: 1961.0,
  192: 1841.0,
  193: 885.0,
  194: 1960.0,
  195: nan,
  196: 1975.0,
  197: 1975.0,
  198: 1993.0,
  199: 1991.0,
  200: 836.0,
  201: 1968.0,
  202: 1976.0,
  203: 1941.0,
  204: nan,
  205: 1960.0,
  206: 1960.0,
  207: 1350.0,
  208: 1991.0,
  209: nan,
  210: 1991.0,
  211: nan,
  212: 1970.0,
  213: 1962.0,
  214: 1956.0,
  215: 1923.0,
  216: 1978.0,
  217: 1945.0,
  218: 1961.0,
  219: 1962.0,
  220: 1991.0,
  221: nan,
  222: 1828.0,
  223: 1776.0,
  224: 1991.0,
  225: 1929.0,
  226: 1979.0,
  227: 1811.0,
  228: nan,
  229: nan,
  230: 1945.0,
  231: 1980.0,
  232: nan,
  233: 1962.0,
  234: 1918.0,
  235: 1918.0,
  236: 1910.0,
  237: 1964.0,
  238: 1980.0},
 'LifeExpectancy': {0: 78.400000000000006,
  1: 45.899999999999999,
  2: 38.299999999999997,
  3: 76.099999999999994,
  4: 71.599999999999994,
  5: 83.5,
  6: 74.700000000000003,
  7: 74.099999999999994,
  8: 75.099999999999994,
  9: 66.400000000000006,
  10: 75.099999999999994,
  11: nan,
  12: nan,
  13: 70.5,
  14: 79.799999999999997,
  15: 77.700000000000003,
  16: 62.899999999999999,
  17: 46.200000000000003,
  18: 77.799999999999997,
  19: 50.200000000000003,
  20: 46.700000000000003,
  21: 60.200000000000003,
  22: 70.900000000000006,
  23: 73.0,
  24: 71.099999999999994,
  25: 71.5,
  26: 68.0,
  27: 70.900000000000006,
  28: 76.900000000000006,
  29: 63.700000000000003,
  30: 62.899999999999999,
  31: 73.0,
  32: 73.599999999999994,
  33: 52.399999999999999,
  34: nan,
  35: 39.299999999999997,
  36: 44.0,
  37: 79.400000000000006,
  38: nan,
  39: 79.599999999999994,
  40: 75.700000000000003,
  41: 71.400000000000006,
  42: 45.200000000000003,
  43: 54.799999999999997,
  44: 48.799999999999997,
  45: 47.399999999999999,
  46: 71.099999999999994,
  47: 70.299999999999997,
  48: 60.0,
  49: 68.900000000000006,
  50: 75.799999999999997,
  51: 76.200000000000003,
  52: nan,
  53: 78.900000000000006,
  54: 76.700000000000003,
  55: 74.5,
  56: 77.400000000000006,
  57: 50.799999999999997,
  58: 73.400000000000006,
  59: 76.5,
  60: 73.200000000000003,
  61: 69.700000000000003,
  62: 71.099999999999994,
  63: 63.299999999999997,
  64: 55.799999999999997,
  65: 49.799999999999997,
  66: 78.799999999999997,
  67: 69.5,
  68: 45.200000000000003,
  69: 77.400000000000006,
  70: 67.900000000000006,
  71: nan,
  72: 78.799999999999997,
  73: 78.400000000000006,
  74: 68.599999999999994,
  75: 50.100000000000001,
  76: 77.700000000000003,
  77: 64.5,
  78: 57.399999999999999,
  79: 79.0,
  80: 45.600000000000001,
  81: 77.0,
  82: 53.200000000000003,
  83: 49.0,
  84: 53.600000000000001,
  85: 78.400000000000006,
  86: 64.5,
  87: 68.099999999999994,
  88: 66.200000000000003,
  89: 76.099999999999994,
  90: 77.799999999999997,
  91: 64.0,
  92: 79.5,
  93: nan,
  94: 69.900000000000006,
  95: 73.700000000000003,
  96: 49.200000000000003,
  97: 71.400000000000006,
  98: 68.0,
  99: 62.5,
  100: nan,
  101: 76.799999999999997,
  102: 69.700000000000003,
  103: 66.5,
  104: 79.400000000000006,
  105: 78.599999999999994,
  106: 79.0,
  107: 75.200000000000003,
  108: 77.400000000000006,
  109: 80.700000000000003,
  110: 63.200000000000003,
  111: 48.0,
  112: 63.399999999999999,
  113: 56.5,
  114: 59.799999999999997,
  115: 70.700000000000003,
  116: 74.400000000000006,
  117: 76.099999999999994,
  118: 53.100000000000001,
  119: 71.299999999999997,
  120: 51.0,
  121: 75.5,
  122: 72.299999999999997,
  123: 78.799999999999997,
  124: 71.799999999999997,
  125: 50.799999999999997,
  126: 69.099999999999994,
  127: 77.099999999999994,
  128: 68.400000000000006,
  129: 81.599999999999994,
  130: 69.099999999999994,
  131: 78.799999999999997,
  132: 64.5,
  133: 55.0,
  134: 62.200000000000003,
  135: 71.5,
  136: 65.5,
  137: 73.799999999999997,
  138: 46.700000000000003,
  139: 77.900000000000006,
  140: 54.899999999999999,
  141: 67.299999999999997,
  142: 75.5,
  143: 37.5,
  144: 50.799999999999997,
  145: 78.0,
  146: 78.299999999999997,
  147: 71.0,
  148: 37.600000000000001,
  149: 70.799999999999997,
  150: 59.5,
  151: 42.5,
  152: 72.799999999999997,
  153: 41.299999999999997,
  154: nan,
  155: 51.600000000000001,
  156: 68.700000000000003,
  157: nan,
  158: 78.299999999999997,
  159: 78.700000000000003,
  160: 57.799999999999997,
  161: 60.799999999999997,
  162: 77.799999999999997,
  163: 71.799999999999997,
  164: 61.100000000000001,
  165: 75.5,
  166: nan,
  167: 70.0,
  168: 67.5,
  169: 68.599999999999994,
  170: 63.100000000000001,
  171: 73.200000000000003,
  172: 75.599999999999994,
  173: 70.700000000000003,
  174: 75.799999999999997,
  175: 73.700000000000003,
  176: 71.400000000000006,
  177: 74.799999999999997,
  178: 72.400000000000006,
  179: 72.700000000000003,
  180: 69.900000000000006,
  181: 67.200000000000003,
  182: 39.299999999999997,
  183: 67.799999999999997,
  184: 56.600000000000001,
  185: 62.200000000000003,
  186: 80.099999999999994,
  187: nan,
  188: 76.799999999999997,
  189: nan,
  190: 71.299999999999997,
  191: 45.299999999999997,
  192: 69.700000000000003,
  193: 81.099999999999994,
  194: 46.200000000000003,
  195: 77.599999999999994,
  196: 65.299999999999997,
  197: 71.400000000000006,
  198: 73.700000000000003,
  199: 74.900000000000006,
  200: 79.599999999999994,
  201: 40.399999999999999,
  202: 70.400000000000006,
  203: 68.5,
  204: 73.299999999999997,
  205: 50.5,
  206: 54.700000000000003,
  207: 68.599999999999994,
  208: 64.099999999999994,
  209: nan,
  210: 60.899999999999999,
  211: 46.0,
  212: 67.900000000000006,
  213: 68.0,
  214: 73.700000000000003,
  215: 71.0,
  216: 66.299999999999997,
  217: 76.400000000000006,
  218: 52.299999999999997,
  219: 42.899999999999999,
  220: 66.0,
  221: nan,
  222: 75.200000000000003,
  223: 77.099999999999994,
  224: 63.700000000000003,
  225: nan,
  226: 72.299999999999997,
  227: 73.099999999999994,
  228: 75.400000000000006,
  229: 78.099999999999994,
  230: 69.299999999999997,
  231: 60.600000000000001,
  232: nan,
  233: 69.200000000000003,
  234: 59.799999999999997,
  235: 72.400000000000006,
  236: 51.100000000000001,
  237: 37.200000000000003,
  238: 37.799999999999997},
 'LocalName': {0: 'Aruba',
  1: 'Afganistan/Afqanestan',
  2: 'Angola',
  3: 'Anguilla',
  4: 'Shqipëria',
  5: 'Andorra',
  6: 'Nederlandse Antillen',
  7: 'Al-Imarat al-´Arabiya al-Muttahida',
  8: 'Argentina',
  9: 'Hajastan',
  10: 'Amerika Samoa',
  11: '–',
  12: 'Terres australes françaises',
  13: 'Antigua and Barbuda',
  14: 'Australia',
  15: 'Österreich',
  16: 'Azärbaycan',
  17: 'Burundi/Uburundi',
  18: 'België/Belgique',
  19: 'Bénin',
  20: 'Burkina Faso',
  21: 'Bangladesh',
  22: 'Balgarija',
  23: 'Al-Bahrayn',
  24: 'The Bahamas',
  25: 'Bosna i Hercegovina',
  26: 'Belarus',
  27: 'Belize',
  28: 'Bermuda',
  29: 'Bolivia',
  30: 'Brasil',
  31: 'Barbados',
  32: 'Brunei Darussalam',
  33: 'Druk-Yul',
  34: 'Bouvetøya',
  35: 'Botswana',
  36: 'Centrafrique/Bê-Afrîka',
  37: 'Canada',
  38: 'Cocos (Keeling) Islands',
  39: 'Schweiz/Suisse/Svizzera/Svizra',
  40: 'Chile',
  41: 'Zhongquo',
  42: 'Côte d’Ivoire',
  43: 'Cameroun/Cameroon',
  44: 'République Démocratique du Congo',
  45: 'Congo',
  46: 'The Cook Islands',
  47: 'Colombia',
  48: 'Komori/Comores',
  49: 'Cabo Verde',
  50: 'Costa Rica',
  51: 'Cuba',
  52: 'Christmas Island',
  53: 'Cayman Islands',
  54: 'Kýpros/Kibris',
  55: '¸esko',
  56: 'Deutschland',
  57: 'Djibouti/Jibuti',
  58: 'Dominica',
  59: 'Danmark',
  60: 'República Dominicana',
  61: 'Al-Jaza’ir/Algérie',
  62: 'Ecuador',
  63: 'Misr',
  64: 'Ertra',
  65: 'As-Sahrawiya',
  66: 'España',
  67: 'Eesti',
  68: 'YeItyop´iya',
  69: 'Suomi',
  70: 'Fiji Islands',
  71: 'Falkland Islands',
  72: 'France',
  73: 'Føroyar',
  74: 'Micronesia',
  75: 'Le Gabon',
  76: 'United Kingdom',
  77: 'Sakartvelo',
  78: 'Ghana',
  79: 'Gibraltar',
  80: 'Guinée',
  81: 'Guadeloupe',
  82: 'The Gambia',
  83: 'Guiné-Bissau',
  84: 'Guinea Ecuatorial',
  85: 'Elláda',
  86: 'Grenada',
  87: 'Kalaallit Nunaat/Grønland',
  88: 'Guatemala',
  89: 'Guyane française',
  90: 'Guam',
  91: 'Guyana',
  92: 'Xianggang/Hong Kong',
  93: 'Heard and McDonald Islands',
  94: 'Honduras',
  95: 'Hrvatska',
  96: 'Haïti/Dayti',
  97: 'Magyarország',
  98: 'Indonesia',
  99: 'Bharat/India',
  100: 'British Indian Ocean Territory',
  101: 'Ireland/Éire',
  102: 'Iran',
  103: 'Al-´Iraq',
  104: 'Ísland',
  105: 'Yisra’el/Isra’il',
  106: 'Italia',
  107: 'Jamaica',
  108: 'Al-Urdunn',
  109: 'Nihon/Nippon',
  110: 'Qazaqstan',
  111: 'Kenya',
  112: 'Kyrgyzstan',
  113: 'Kâmpuchéa',
  114: 'Kiribati',
  115: 'Saint Kitts and Nevis',
  116: 'Taehan Min’guk (Namhan)',
  117: 'Al-Kuwayt',
  118: 'Lao',
  119: 'Lubnan',
  120: 'Liberia',
  121: 'Libiya',
  122: 'Saint Lucia',
  123: 'Liechtenstein',
  124: 'Sri Lanka/Ilankai',
  125: 'Lesotho',
  126: 'Lietuva',
  127: 'Luxembourg/Lëtzebuerg',
  128: 'Latvija',
  129: 'Macau/Aomen',
  130: 'Al-Maghrib',
  131: 'Monaco',
  132: 'Moldova',
  133: 'Madagasikara/Madagascar',
  134: 'Dhivehi Raajje/Maldives',
  135: 'México',
  136: 'Marshall Islands/Majol',
  137: 'Makedonija',
  138: 'Mali',
  139: 'Malta',
  140: 'Myanma Pye',
  141: 'Mongol Uls',
  142: 'Northern Mariana Islands',
  143: 'Moçambique',
  144: 'Muritaniya/Mauritanie',
  145: 'Montserrat',
  146: 'Martinique',
  147: 'Mauritius',
  148: 'Malawi',
  149: 'Malaysia',
  150: 'Mayotte',
  151: 'Namibia',
  152: 'Nouvelle-Calédonie',
  153: 'Niger',
  154: 'Norfolk Island',
  155: 'Nigeria',
  156: 'Nicaragua',
  157: 'Niue',
  158: 'Nederland',
  159: 'Norge',
  160: 'Nepal',
  161: 'Naoero/Nauru',
  162: 'New Zealand/Aotearoa',
  163: '´Uman',
  164: 'Pakistan',
  165: 'Panamá',
  166: 'Pitcairn',
  167: 'Perú/Piruw',
  168: 'Pilipinas',
  169: 'Belau/Palau',
  170: 'Papua New Guinea/Papua Niugini',
  171: 'Polska',
  172: 'Puerto Rico',
  173: 'Choson Minjujuui In´min Konghwaguk (Bukhan)',
  174: 'Portugal',
  175: 'Paraguay',
  176: 'Filastin',
  177: 'Polynésie française',
  178: 'Qatar',
  179: 'Réunion',
  180: 'România',
  181: 'Rossija',
  182: 'Rwanda/Urwanda',
  183: 'Al-´Arabiya as-Sa´udiya',
  184: 'As-Sudan',
  185: 'Sénégal/Sounougal',
  186: 'Singapore/Singapura/Xinjiapo/Singapur',
  187: 'South Georgia and the South Sandwich Islands',
  188: 'Saint Helena',
  189: 'Svalbard og Jan Mayen',
  190: 'Solomon Islands',
  191: 'Sierra Leone',
  192: 'El Salvador',
  193: 'San Marino',
  194: 'Soomaaliya',
  195: 'Saint-Pierre-et-Miquelon',
  196: 'São Tomé e Príncipe',
  197: 'Suriname',
  198: 'Slovensko',
  199: 'Slovenija',
  200: 'Sverige',
  201: 'kaNgwane',
  202: 'Sesel/Seychelles',
  203: 'Suriya',
  204: 'The Turks and Caicos Islands',
  205: 'Tchad/Tshad',
  206: 'Togo',
  207: 'Prathet Thai',
  208: 'Toçikiston',
  209: 'Tokelau',
  210: 'Türkmenostan',
  211: 'Timor Timur',
  212: 'Tonga',
  213: 'Trinidad and Tobago',
  214: 'Tunis/Tunisie',
  215: 'Türkiye',
  216: 'Tuvalu',
  217: 'T’ai-wan',
  218: 'Tanzania',
  219: 'Uganda',
  220: 'Ukrajina',
  221: 'United States Minor Outlying Islands',
  222: 'Uruguay',
  223: 'United States',
  224: 'Uzbekiston',
  225: 'Santa Sede/Città del Vaticano',
  226: 'Saint Vincent and the Grenadines',
  227: 'Venezuela',
  228: 'British Virgin Islands',
  229: 'Virgin Islands of the United States',
  230: 'Viêt Nam',
  231: 'Vanuatu',
  232: 'Wallis-et-Futuna',
  233: 'Samoa',
  234: 'Al-Yaman',
  235: 'Jugoslavija',
  236: 'South Africa',
  237: 'Zambia',
  238: 'Zimbabwe'},
 'Name': {0: 'Aruba',
  1: 'Afghanistan',
  2: 'Angola',
  3: 'Anguilla',
  4: 'Albania',
  5: 'Andorra',
  6: 'Netherlands Antilles',
  7: 'United Arab Emirates',
  8: 'Argentina',
  9: 'Armenia',
  10: 'American Samoa',
  11: 'Antarctica',
  12: 'French Southern territories',
  13: 'Antigua and Barbuda',
  14: 'Australia',
  15: 'Austria',
  16: 'Azerbaijan',
  17: 'Burundi',
  18: 'Belgium',
  19: 'Benin',
  20: 'Burkina Faso',
  21: 'Bangladesh',
  22: 'Bulgaria',
  23: 'Bahrain',
  24: 'Bahamas',
  25: 'Bosnia and Herzegovina',
  26: 'Belarus',
  27: 'Belize',
  28: 'Bermuda',
  29: 'Bolivia',
  30: 'Brazil',
  31: 'Barbados',
  32: 'Brunei',
  33: 'Bhutan',
  34: 'Bouvet Island',
  35: 'Botswana',
  36: 'Central African Republic',
  37: 'Canada',
  38: 'Cocos (Keeling) Islands',
  39: 'Switzerland',
  40: 'Chile',
  41: 'China',
  42: 'Côte d’Ivoire',
  43: 'Cameroon',
  44: 'Congo, The Democratic Republic of the',
  45: 'Congo',
  46: 'Cook Islands',
  47: 'Colombia',
  48: 'Comoros',
  49: 'Cape Verde',
  50: 'Costa Rica',
  51: 'Cuba',
  52: 'Christmas Island',
  53: 'Cayman Islands',
  54: 'Cyprus',
  55: 'Czech Republic',
  56: 'Germany',
  57: 'Djibouti',
  58: 'Dominica',
  59: 'Denmark',
  60: 'Dominican Republic',
  61: 'Algeria',
  62: 'Ecuador',
  63: 'Egypt',
  64: 'Eritrea',
  65: 'Western Sahara',
  66: 'Spain',
  67: 'Estonia',
  68: 'Ethiopia',
  69: 'Finland',
  70: 'Fiji Islands',
  71: 'Falkland Islands',
  72: 'France',
  73: 'Faroe Islands',
  74: 'Micronesia, Federated States of',
  75: 'Gabon',
  76: 'United Kingdom',
  77: 'Georgia',
  78: 'Ghana',
  79: 'Gibraltar',
  80: 'Guinea',
  81: 'Guadeloupe',
  82: 'Gambia',
  83: 'Guinea-Bissau',
  84: 'Equatorial Guinea',
  85: 'Greece',
  86: 'Grenada',
  87: 'Greenland',
  88: 'Guatemala',
  89: 'French Guiana',
  90: 'Guam',
  91: 'Guyana',
  92: 'Hong Kong',
  93: 'Heard Island and McDonald Islands',
  94: 'Honduras',
  95: 'Croatia',
  96: 'Haiti',
  97: 'Hungary',
  98: 'Indonesia',
  99: 'India',
  100: 'British Indian Ocean Territory',
  101: 'Ireland',
  102: 'Iran',
  103: 'Iraq',
  104: 'Iceland',
  105: 'Israel',
  106: 'Italy',
  107: 'Jamaica',
  108: 'Jordan',
  109: 'Japan',
  110: 'Kazakstan',
  111: 'Kenya',
  112: 'Kyrgyzstan',
  113: 'Cambodia',
  114: 'Kiribati',
  115: 'Saint Kitts and Nevis',
  116: 'South Korea',
  117: 'Kuwait',
  118: 'Laos',
  119: 'Lebanon',
  120: 'Liberia',
  121: 'Libyan Arab Jamahiriya',
  122: 'Saint Lucia',
  123: 'Liechtenstein',
  124: 'Sri Lanka',
  125: 'Lesotho',
  126: 'Lithuania',
  127: 'Luxembourg',
  128: 'Latvia',
  129: 'Macao',
  130: 'Morocco',
  131: 'Monaco',
  132: 'Moldova',
  133: 'Madagascar',
  134: 'Maldives',
  135: 'Mexico',
  136: 'Marshall Islands',
  137: 'Macedonia',
  138: 'Mali',
  139: 'Malta',
  140: 'Myanmar',
  141: 'Mongolia',
  142: 'Northern Mariana Islands',
  143: 'Mozambique',
  144: 'Mauritania',
  145: 'Montserrat',
  146: 'Martinique',
  147: 'Mauritius',
  148: 'Malawi',
  149: 'Malaysia',
  150: 'Mayotte',
  151: 'Namibia',
  152: 'New Caledonia',
  153: 'Niger',
  154: 'Norfolk Island',
  155: 'Nigeria',
  156: 'Nicaragua',
  157: 'Niue',
  158: 'Netherlands',
  159: 'Norway',
  160: 'Nepal',
  161: 'Nauru',
  162: 'New Zealand',
  163: 'Oman',
  164: 'Pakistan',
  165: 'Panama',
  166: 'Pitcairn',
  167: 'Peru',
  168: 'Philippines',
  169: 'Palau',
  170: 'Papua New Guinea',
  171: 'Poland',
  172: 'Puerto Rico',
  173: 'North Korea',
  174: 'Portugal',
  175: 'Paraguay',
  176: 'Palestine',
  177: 'French Polynesia',
  178: 'Qatar',
  179: 'Réunion',
  180: 'Romania',
  181: 'Russian Federation',
  182: 'Rwanda',
  183: 'Saudi Arabia',
  184: 'Sudan',
  185: 'Senegal',
  186: 'Singapore',
  187: 'South Georgia and the South Sandwich Islands',
  188: 'Saint Helena',
  189: 'Svalbard and Jan Mayen',
  190: 'Solomon Islands',
  191: 'Sierra Leone',
  192: 'El Salvador',
  193: 'San Marino',
  194: 'Somalia',
  195: 'Saint Pierre and Miquelon',
  196: 'Sao Tome and Principe',
  197: 'Suriname',
  198: 'Slovakia',
  199: 'Slovenia',
  200: 'Sweden',
  201: 'Swaziland',
  202: 'Seychelles',
  203: 'Syria',
  204: 'Turks and Caicos Islands',
  205: 'Chad',
  206: 'Togo',
  207: 'Thailand',
  208: 'Tajikistan',
  209: 'Tokelau',
  210: 'Turkmenistan',
  211: 'East Timor',
  212: 'Tonga',
  213: 'Trinidad and Tobago',
  214: 'Tunisia',
  215: 'Turkey',
  216: 'Tuvalu',
  217: 'Taiwan',
  218: 'Tanzania',
  219: 'Uganda',
  220: 'Ukraine',
  221: 'United States Minor Outlying Islands',
  222: 'Uruguay',
  223: 'United States',
  224: 'Uzbekistan',
  225: 'Holy See (Vatican City State)',
  226: 'Saint Vincent and the Grenadines',
  227: 'Venezuela',
  228: 'Virgin Islands, British',
  229: 'Virgin Islands, U.S.',
  230: 'Vietnam',
  231: 'Vanuatu',
  232: 'Wallis and Futuna',
  233: 'Samoa',
  234: 'Yemen',
  235: 'Yugoslavia',
  236: 'South Africa',
  237: 'Zambia',
  238: 'Zimbabwe'},
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 'Region': {0: 'Caribbean',
  1: 'Southern and Central Asia',
  2: 'Central Africa',
  3: 'Caribbean',
  4: 'Southern Europe',
  5: 'Southern Europe',
  6: 'Caribbean',
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  63: 'Northern Africa',
  64: 'Eastern Africa',
  65: 'Northern Africa',
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  68: 'Eastern Africa',
  69: 'Nordic Countries',
  70: 'Melanesia',
  71: 'South America',
  72: 'Western Europe',
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  75: 'Central Africa',
  76: 'British Islands',
  77: 'Middle East',
  78: 'Western Africa',
  79: 'Southern Europe',
  80: 'Western Africa',
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  82: 'Western Africa',
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In [87]:

    
city_groups = city_df.groupby("CountryCode")

country_list = []


for index, row in country_df.iterrows():
    country_dict = {}
    
    country_code = row["Code"]
#     city_group_df = city_groups.get_group(country_code) 위랑 같아
    country_dict["name"] = row["Name"]
    country_dict["population"] = row["Population"]
    country_dict["cities"] = []  # cities 라는 key 로 빈 리스트를 초기화 한다.
        
    if country_code in city_df["CountryCode"].unique():
        city_group_df = city_groups.get_group(country_code)
        
        for city_index, city_row in city_group_df.iterrows():
            city_dict = {}
            city_dict["name"] = city_row["Name"]
            city_dict["population"] = city_row["Population"]
            country_dict["cities"].append(city_dict)
            
    country_list.append(country_dict)



In [88]:

    
country_list









    Out[88]:





[{'cities': [{'name': 'Oranjestad', 'population': 29034}],
  'name': 'Aruba',
  'population': 103000},
 {'cities': [{'name': 'Kabul', 'population': 1780000},
   {'name': 'Qandahar', 'population': 237500},
   {'name': 'Herat', 'population': 186800},
   {'name': 'Mazar-e-Sharif', 'population': 127800}],
  'name': 'Afghanistan',
  'population': 22720000},
 {'cities': [{'name': 'Luanda', 'population': 2022000},
   {'name': 'Huambo', 'population': 163100},
   {'name': 'Lobito', 'population': 130000},
   {'name': 'Benguela', 'population': 128300},
   {'name': 'Namibe', 'population': 118200}],
  'name': 'Angola',
  'population': 12878000},
 {'cities': [{'name': 'South Hill', 'population': 961},
   {'name': 'The Valley', 'population': 595}],
  'name': 'Anguilla',
  'population': 8000},
 {'cities': [{'name': 'Tirana', 'population': 270000}],
  'name': 'Albania',
  'population': 3401200},
 {'cities': [{'name': 'Andorra la Vella', 'population': 21189}],
  'name': 'Andorra',
  'population': 78000},
 {'cities': [{'name': 'Willemstad', 'population': 2345}],
  'name': 'Netherlands Antilles',
  'population': 217000},
 {'cities': [{'name': 'Dubai', 'population': 669181},
   {'name': 'Abu Dhabi', 'population': 398695},
   {'name': 'Sharja', 'population': 320095},
   {'name': 'al-Ayn', 'population': 225970},
   {'name': 'Ajman', 'population': 114395}],
  'name': 'United Arab Emirates',
  'population': 2441000},
 {'cities': [{'name': 'Buenos Aires', 'population': 2982146},
   {'name': 'La Matanza', 'population': 1266461},
   {'name': 'Córdoba', 'population': 1157507},
   {'name': 'Rosario', 'population': 907718},
   {'name': 'Lomas de Zamora', 'population': 622013},
   {'name': 'Quilmes', 'population': 559249},
   {'name': 'Almirante Brown', 'population': 538918},
   {'name': 'La Plata', 'population': 521936},
   {'name': 'Mar del Plata', 'population': 512880},
   {'name': 'San Miguel de Tucumán', 'population': 470809},
   {'name': 'Lanús', 'population': 469735},
   {'name': 'Merlo', 'population': 463846},
   {'name': 'General San Martín', 'population': 422542},
   {'name': 'Salta', 'population': 367550},
   {'name': 'Moreno', 'population': 356993},
   {'name': 'Santa Fé', 'population': 353063},
   {'name': 'Avellaneda', 'population': 353046},
   {'name': 'Tres de Febrero', 'population': 352311},
   {'name': 'Morón', 'population': 349246},
   {'name': 'Florencio Varela', 'population': 315432},
   {'name': 'San Isidro', 'population': 306341},
   {'name': 'Tigre', 'population': 296226},
   {'name': 'Malvinas Argentinas', 'population': 290335},
   {'name': 'Vicente López', 'population': 288341},
   {'name': 'Berazategui', 'population': 276916},
   {'name': 'Corrientes', 'population': 258103},
   {'name': 'San Miguel', 'population': 248700},
   {'name': 'Bahía Blanca', 'population': 239810},
   {'name': 'Esteban Echeverría', 'population': 235760},
   {'name': 'Resistencia', 'population': 229212},
   {'name': 'José C. Paz', 'population': 221754},
   {'name': 'Paraná', 'population': 207041},
   {'name': 'Godoy Cruz', 'population': 206998},
   {'name': 'Posadas', 'population': 201273},
   {'name': 'Guaymallén', 'population': 200595},
   {'name': 'Santiago del Estero', 'population': 189947},
   {'name': 'San Salvador de Jujuy', 'population': 178748},
   {'name': 'Hurlingham', 'population': 170028},
   {'name': 'Neuquén', 'population': 167296},
   {'name': 'Ituzaingó', 'population': 158197},
   {'name': 'San Fernando', 'population': 153036},
   {'name': 'Formosa', 'population': 147636},
   {'name': 'Las Heras', 'population': 145823},
   {'name': 'La Rioja', 'population': 138117},
   {'name': 'San Fernando del Valle de Cata', 'population': 134935},
   {'name': 'Río Cuarto', 'population': 134355},
   {'name': 'Comodoro Rivadavia', 'population': 124104},
   {'name': 'Mendoza', 'population': 123027},
   {'name': 'San Nicolás de los Arroyos', 'population': 119302},
   {'name': 'San Juan', 'population': 119152},
   {'name': 'Escobar', 'population': 116675},
   {'name': 'Concordia', 'population': 116485},
   {'name': 'Pilar', 'population': 113428},
   {'name': 'San Luis', 'population': 110136},
   {'name': 'Ezeiza', 'population': 99578},
   {'name': 'San Rafael', 'population': 94651},
   {'name': 'Tandil', 'population': 91101}],
  'name': 'Argentina',
  'population': 37032000},
 {'cities': [{'name': 'Yerevan', 'population': 1248700},
   {'name': 'Gjumri', 'population': 211700},
   {'name': 'Vanadzor', 'population': 172700}],
  'name': 'Armenia',
  'population': 3520000},
 {'cities': [{'name': 'Tafuna', 'population': 5200},
   {'name': 'Fagatogo', 'population': 2323}],
  'name': 'American Samoa',
  'population': 68000},
 {'cities': [], 'name': 'Antarctica', 'population': 0},
 {'cities': [], 'name': 'French Southern territories', 'population': 0},
 {'cities': [{'name': 'Saint John´s', 'population': 24000}],
  'name': 'Antigua and Barbuda',
  'population': 68000},
 {'cities': [{'name': 'Sydney', 'population': 3276207},
   {'name': 'Melbourne', 'population': 2865329},
   {'name': 'Brisbane', 'population': 1291117},
   {'name': 'Perth', 'population': 1096829},
   {'name': 'Adelaide', 'population': 978100},
   {'name': 'Canberra', 'population': 322723},
   {'name': 'Gold Coast', 'population': 311932},
   {'name': 'Newcastle', 'population': 270324},
   {'name': 'Central Coast', 'population': 227657},
   {'name': 'Wollongong', 'population': 219761},
   {'name': 'Hobart', 'population': 126118},
   {'name': 'Geelong', 'population': 125382},
   {'name': 'Townsville', 'population': 109914},
   {'name': 'Cairns', 'population': 92273}],
  'name': 'Australia',
  'population': 18886000},
 {'cities': [{'name': 'Wien', 'population': 1608144},
   {'name': 'Graz', 'population': 240967},
   {'name': 'Linz', 'population': 188022},
   {'name': 'Salzburg', 'population': 144247},
   {'name': 'Innsbruck', 'population': 111752},
   {'name': 'Klagenfurt', 'population': 91141}],
  'name': 'Austria',
  'population': 8091800},
 {'cities': [{'name': 'Baku', 'population': 1787800},
   {'name': 'Gäncä', 'population': 299300},
   {'name': 'Sumqayit', 'population': 283000},
   {'name': 'Mingäçevir', 'population': 93900}],
  'name': 'Azerbaijan',
  'population': 7734000},
 {'cities': [{'name': 'Bujumbura', 'population': 300000}],
  'name': 'Burundi',
  'population': 6695000},
 {'cities': [{'name': 'Antwerpen', 'population': 446525},
   {'name': 'Gent', 'population': 224180},
   {'name': 'Charleroi', 'population': 200827},
   {'name': 'Liège', 'population': 185639},
   {'name': 'Bruxelles [Brussel]', 'population': 133859},
   {'name': 'Brugge', 'population': 116246},
   {'name': 'Schaerbeek', 'population': 105692},
   {'name': 'Namur', 'population': 105419},
   {'name': 'Mons', 'population': 90935}],
  'name': 'Belgium',
  'population': 10239000},
 {'cities': [{'name': 'Cotonou', 'population': 536827},
   {'name': 'Porto-Novo', 'population': 194000},
   {'name': 'Djougou', 'population': 134099},
   {'name': 'Parakou', 'population': 103577}],
  'name': 'Benin',
  'population': 6097000},
 {'cities': [{'name': 'Ouagadougou', 'population': 824000},
   {'name': 'Bobo-Dioulasso', 'population': 300000},
   {'name': 'Koudougou', 'population': 105000}],
  'name': 'Burkina Faso',
  'population': 11937000},
 {'cities': [{'name': 'Dhaka', 'population': 3612850},
   {'name': 'Chittagong', 'population': 1392860},
   {'name': 'Khulna', 'population': 663340},
   {'name': 'Rajshahi', 'population': 294056},
   {'name': 'Narayanganj', 'population': 202134},
   {'name': 'Rangpur', 'population': 191398},
   {'name': 'Mymensingh', 'population': 188713},
   {'name': 'Barisal', 'population': 170232},
   {'name': 'Tungi', 'population': 168702},
   {'name': 'Jessore', 'population': 139710},
   {'name': 'Comilla', 'population': 135313},
   {'name': 'Nawabganj', 'population': 130577},
   {'name': 'Dinajpur', 'population': 127815},
   {'name': 'Bogra', 'population': 120170},
   {'name': 'Sylhet', 'population': 117396},
   {'name': 'Brahmanbaria', 'population': 109032},
   {'name': 'Tangail', 'population': 106004},
   {'name': 'Jamalpur', 'population': 103556},
   {'name': 'Pabna', 'population': 103277},
   {'name': 'Naogaon', 'population': 101266},
   {'name': 'Sirajganj', 'population': 99669},
   {'name': 'Narsinghdi', 'population': 98342},
   {'name': 'Saidpur', 'population': 96777},
   {'name': 'Gazipur', 'population': 96717}],
  'name': 'Bangladesh',
  'population': 129155000},
 {'cities': [{'name': 'Sofija', 'population': 1122302},
   {'name': 'Plovdiv', 'population': 342584},
   {'name': 'Varna', 'population': 299801},
   {'name': 'Burgas', 'population': 195255},
   {'name': 'Ruse', 'population': 166467},
   {'name': 'Stara Zagora', 'population': 147939},
   {'name': 'Pleven', 'population': 121952},
   {'name': 'Sliven', 'population': 105530},
   {'name': 'Dobric', 'population': 100399},
   {'name': 'Šumen', 'population': 94686}],
  'name': 'Bulgaria',
  'population': 8190900},
 {'cities': [{'name': 'al-Manama', 'population': 148000}],
  'name': 'Bahrain',
  'population': 617000},
 {'cities': [{'name': 'Nassau', 'population': 172000}],
  'name': 'Bahamas',
  'population': 307000},
 {'cities': [{'name': 'Sarajevo', 'population': 360000},
   {'name': 'Banja Luka', 'population': 143079},
   {'name': 'Zenica', 'population': 96027}],
  'name': 'Bosnia and Herzegovina',
  'population': 3972000},
 {'cities': [{'name': 'Minsk', 'population': 1674000},
   {'name': 'Gomel', 'population': 475000},
   {'name': 'Mogiljov', 'population': 356000},
   {'name': 'Vitebsk', 'population': 340000},
   {'name': 'Grodno', 'population': 302000},
   {'name': 'Brest', 'population': 286000},
   {'name': 'Bobruisk', 'population': 221000},
   {'name': 'Baranovitši', 'population': 167000},
   {'name': 'Borisov', 'population': 151000},
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   {'name': 'Orša', 'population': 124000},
   {'name': 'Mozyr', 'population': 110000},
   {'name': 'Novopolotsk', 'population': 106000},
   {'name': 'Lida', 'population': 101000},
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  'name': 'Belarus',
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   {'name': 'São João de Meriti', 'population': 440052},
   {'name': 'Londrina', 'population': 432257},
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   {'name': 'Suez', 'population': 417610},
   {'name': 'al-Mahallat al-Kubra', 'population': 395402},
   {'name': 'Tanta', 'population': 371010},
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   {'name': 'Bahtim', 'population': 275807},
   {'name': 'Zagazig', 'population': 267351},
   {'name': 'al-Faiyum', 'population': 260964},
   {'name': 'Ismailia', 'population': 254477},
   {'name': 'Kafr al-Dawwar', 'population': 231978},
   {'name': 'Assuan', 'population': 219017},
   {'name': 'Damanhur', 'population': 212203},
   {'name': 'al-Minya', 'population': 201360},
   {'name': 'Bani Suwayf', 'population': 172032},
   {'name': 'Qina', 'population': 171275},
   {'name': 'Sawhaj', 'population': 170125},
   {'name': 'Shibin al-Kawm', 'population': 159909},
   {'name': 'Bulaq al-Dakrur', 'population': 148787},
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   {'name': 'Mit Ghamr', 'population': 101801},
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   {'name': 'Talkha', 'population': 97700},
   {'name': 'Qalyub', 'population': 97200},
   {'name': 'Jirja', 'population': 95400},
   {'name': 'Idfu', 'population': 94200},
   {'name': 'al-Hawamidiya', 'population': 91700},
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   {'name': 'Zaragoza', 'population': 603367},
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   {'name': 'Valladolid', 'population': 319998},
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   {'name': 'Gijón', 'population': 267980},
   {'name': 'L´Hospitalet de Llobregat', 'population': 247986},
   {'name': 'Granada', 'population': 244767},
   {'name': 'A Coruña (La Coruña)', 'population': 243402},
   {'name': 'Vitoria-Gasteiz', 'population': 217154},
   {'name': 'Santa Cruz de Tenerife', 'population': 213050},
   {'name': 'Badalona', 'population': 209635},
   {'name': 'Oviedo', 'population': 200453},
   {'name': 'Móstoles', 'population': 195351},
   {'name': 'Elche [Elx]', 'population': 193174},
   {'name': 'Sabadell', 'population': 184859},
   {'name': 'Santander', 'population': 184165},
   {'name': 'Jerez de la Frontera', 'population': 182660},
   {'name': 'Pamplona [Iruña]', 'population': 180483},
   {'name': 'Donostia-San Sebastián', 'population': 179208},
   {'name': 'Cartagena', 'population': 177709},
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   {'name': 'Burgos', 'population': 162802},
   {'name': 'Salamanca', 'population': 158720},
   {'name': 'Albacete', 'population': 147527},
   {'name': 'Getafe', 'population': 145371},
   {'name': 'Cádiz', 'population': 142449},
   {'name': 'Alcorcón', 'population': 142048},
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   {'name': 'Castellón de la Plana [Castell', 'population': 139712},
   {'name': 'Badajoz', 'population': 136613},
   {'name': '[San Cristóbal de] la Laguna', 'population': 127945},
   {'name': 'Logroño', 'population': 127093},
   {'name': 'Santa Coloma de Gramenet', 'population': 120802},
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   {'name': 'Mataró', 'population': 104095},
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   {'name': 'Barakaldo', 'population': 98212},
   {'name': 'Dos Hermanas', 'population': 94591},
   {'name': 'Santiago de Compostela', 'population': 93745},
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   {'name': 'Dese', 'population': 97314},
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   {'name': 'Tampere', 'population': 195468},
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  'name': 'Fiji Islands',
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   {'name': 'Reims', 'population': 187206},
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   {'name': 'St-Étienne', 'population': 180210},
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   {'name': 'Edinburgh', 'population': 450180},
   {'name': 'Sheffield', 'population': 431607},
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   {'name': 'Kingston upon Hull', 'population': 262000},
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   {'name': 'Stoke-on-Trent', 'population': 252000},
   {'name': 'Wolverhampton', 'population': 242000},
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   {'name': 'Swansea', 'population': 230000},
   {'name': 'Southampton', 'population': 216000},
   {'name': 'Aberdeen', 'population': 213070},
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   {'name': 'Walsall', 'population': 174739},
   {'name': 'Bournemouth', 'population': 162000},
   {'name': 'Peterborough', 'population': 156000},
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   {'name': 'Blackpool', 'population': 151000},
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   {'name': 'Reading', 'population': 148000},
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   {'name': 'Oxford', 'population': 144000},
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   {'name': 'Gloucester', 'population': 107000},
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   {'name': 'Pangkal Pinang', 'population': 124000},
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   {'name': 'Cibinong', 'population': 101300},
   {'name': 'Palangka Raya', 'population': 99693},
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   {'name': 'Garut', 'population': 95800},
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   {'name': 'Kalyan', 'population': 1014557},
   {'name': 'Madurai', 'population': 977856},
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   {'name': 'Huimanguillo', 'population': 158335},
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   {'name': 'Poza Rica de Hidalgo', 'population': 152678},
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   {'name': 'San Pedro Garza García', 'population': 126147},
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   {'name': 'Manzanillo', 'population': 124014},
   {'name': 'Iguala de la Independencia', 'population': 123883},
   {'name': 'Zacatecas', 'population': 123700},
   {'name': 'Tlajomulco de Zúñiga', 'population': 123220},
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   {'name': 'Zinacantepec', 'population': 121715},
   {'name': 'San Martín Texmelucan', 'population': 121093},
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   {'name': 'Martínez de la Torre', 'population': 118815},
   {'name': 'Orizaba', 'population': 118488},
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   {'name': 'Atlixco', 'population': 117019},
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   {'name': 'Ixtlahuaca', 'population': 115548},
   {'name': 'El Mante', 'population': 112453},
   {'name': 'Lerdo', 'population': 112272},
   {'name': 'Almoloya de Juárez', 'population': 110550},
   {'name': 'Acámbaro', 'population': 110487},
   {'name': 'Acuña', 'population': 110388},
   {'name': 'Guadalupe', 'population': 108881},
   {'name': 'Huejutla de Reyes', 'population': 108017},
   {'name': 'Hidalgo', 'population': 106198},
   {'name': 'Los Cabos', 'population': 105199},
   {'name': 'Comitán de Domínguez', 'population': 104986},
   {'name': 'Cunduacán', 'population': 104164},
   {'name': 'Río Bravo', 'population': 103901},
   {'name': 'Temapache', 'population': 102824},
   {'name': 'Chilapa de Alvarez', 'population': 102716},
   {'name': 'Hidalgo del Parral', 'population': 100881},
   {'name': 'San Francisco del Rincón', 'population': 100149},
   {'name': 'Taxco de Alarcón', 'population': 99907},
   {'name': 'Zumpango', 'population': 99781},
   {'name': 'San Pedro Cholula', 'population': 99734},
   {'name': 'Lerma', 'population': 99714},
   {'name': 'Tecomán', 'population': 99296},
   {'name': 'Las Margaritas', 'population': 97389},
   {'name': 'Cosoleacaque', 'population': 97199},
   {'name': 'San Luis de la Paz', 'population': 96763},
   {'name': 'José Azueta', 'population': 95448},
   {'name': 'Santiago Ixcuintla', 'population': 95311},
   {'name': 'San Felipe', 'population': 95305},
   {'name': 'Tejupilco', 'population': 94934},
   {'name': 'Tantoyuca', 'population': 94709},
   {'name': 'Salvatierra', 'population': 94322},
   {'name': 'Tultepec', 'population': 93364},
   {'name': 'Temixco', 'population': 92686},
   {'name': 'Matamoros', 'population': 91858},
   {'name': 'Pánuco', 'population': 90551},
   {'name': 'El Fuerte', 'population': 89556},
   {'name': 'Tierra Blanca', 'population': 89143}],
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  'name': 'Marshall Islands',
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  'name': 'Macedonia',
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  'name': 'Mali',
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   {'name': 'Valletta', 'population': 7073}],
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   {'name': 'Mandalay', 'population': 885300},
   {'name': 'Moulmein (Mawlamyine)', 'population': 307900},
   {'name': 'Pegu (Bago)', 'population': 190900},
   {'name': 'Bassein (Pathein)', 'population': 183900},
   {'name': 'Monywa', 'population': 138600},
   {'name': 'Sittwe (Akyab)', 'population': 137600},
   {'name': 'Taunggyi (Taunggye)', 'population': 131500},
   {'name': 'Meikhtila', 'population': 129700},
   {'name': 'Mergui (Myeik)', 'population': 122700},
   {'name': 'Lashio (Lasho)', 'population': 107600},
   {'name': 'Prome (Pyay)', 'population': 105700},
   {'name': 'Henzada (Hinthada)', 'population': 104700},
   {'name': 'Myingyan', 'population': 103600},
   {'name': 'Tavoy (Dawei)', 'population': 96800},
   {'name': 'Pagakku (Pakokku)', 'population': 94800}],
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  'name': 'Mongolia',
  'population': 2662000},
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  'name': 'Northern Mariana Islands',
  'population': 78000},
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   {'name': 'Matola', 'population': 424662},
   {'name': 'Beira', 'population': 397368},
   {'name': 'Nampula', 'population': 303346},
   {'name': 'Chimoio', 'population': 171056},
   {'name': 'Naçala-Porto', 'population': 158248},
   {'name': 'Quelimane', 'population': 150116},
   {'name': 'Mocuba', 'population': 124700},
   {'name': 'Tete', 'population': 101984},
   {'name': 'Xai-Xai', 'population': 99442},
   {'name': 'Gurue', 'population': 99300},
   {'name': 'Maxixe', 'population': 93985}],
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  'population': 19680000},
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   {'name': 'Nouâdhibou', 'population': 97600}],
  'name': 'Mauritania',
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  'name': 'Montserrat',
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  'name': 'Martinique',
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   {'name': 'Beau Bassin-Rose Hill', 'population': 100616},
   {'name': 'Vacoas-Phoenix', 'population': 98464}],
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   {'name': 'Lilongwe', 'population': 435964}],
  'name': 'Malawi',
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   {'name': 'Johor Baharu', 'population': 328436},
   {'name': 'Petaling Jaya', 'population': 254350},
   {'name': 'Kelang', 'population': 243355},
   {'name': 'Kuala Terengganu', 'population': 228119},
   {'name': 'Pinang', 'population': 219603},
   {'name': 'Kota Bharu', 'population': 219582},
   {'name': 'Kuantan', 'population': 199484},
   {'name': 'Taiping', 'population': 183261},
   {'name': 'Seremban', 'population': 182869},
   {'name': 'Kuching', 'population': 148059},
   {'name': 'Sibu', 'population': 126381},
   {'name': 'Sandakan', 'population': 125841},
   {'name': 'Alor Setar', 'population': 124412},
   {'name': 'Selayang Baru', 'population': 124228},
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   {'name': 'Shah Alam', 'population': 102019}],
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   {'name': 'Zinder', 'population': 120892},
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   {'name': 'Ogbomosho', 'population': 730000},
   {'name': 'Kano', 'population': 674100},
   {'name': 'Oshogbo', 'population': 476800},
   {'name': 'Ilorin', 'population': 475800},
   {'name': 'Abeokuta', 'population': 427400},
   {'name': 'Port Harcourt', 'population': 410000},
   {'name': 'Zaria', 'population': 379200},
   {'name': 'Ilesha', 'population': 378400},
   {'name': 'Onitsha', 'population': 371900},
   {'name': 'Iwo', 'population': 362000},
   {'name': 'Ado-Ekiti', 'population': 359400},
   {'name': 'Abuja', 'population': 350100},
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   {'name': 'Mushin', 'population': 333200},
   {'name': 'Maiduguri', 'population': 320000},
   {'name': 'Enugu', 'population': 316100},
   {'name': 'Ede', 'population': 307100},
   {'name': 'Aba', 'population': 298900},
   {'name': 'Ife', 'population': 296800},
   {'name': 'Ila', 'population': 264000},
   {'name': 'Oyo', 'population': 256400},
   {'name': 'Ikerre', 'population': 244600},
   {'name': 'Benin City', 'population': 229400},
   {'name': 'Iseyin', 'population': 217300},
   {'name': 'Katsina', 'population': 206500},
   {'name': 'Jos', 'population': 206300},
   {'name': 'Sokoto', 'population': 204900},
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   {'name': 'Offa', 'population': 197200},
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   {'name': 'Shaki', 'population': 174500},
   {'name': 'Calabar', 'population': 174400},
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   {'name': 'Ijebu-Ode', 'population': 156400},
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   {'name': 'Kumo', 'population': 148000},
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   {'name': 'Sapele', 'population': 139200},
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   {'name': 'Warri', 'population': 126100},
   {'name': 'Bida', 'population': 125500},
   {'name': 'Ikire', 'population': 123300},
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   {'name': 'Lafia', 'population': 122500},
   {'name': 'Inisa', 'population': 119800},
   {'name': 'Shagamu', 'population': 117200},
   {'name': 'Awka', 'population': 111200},
   {'name': 'Gombe', 'population': 107800},
   {'name': 'Igboho', 'population': 106800},
   {'name': 'Ejigbo', 'population': 105900},
   {'name': 'Agege', 'population': 105000},
   {'name': 'Ise-Ekiti', 'population': 103400},
   {'name': 'Ugep', 'population': 102600},
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   {'name': 'Chinandega', 'population': 97387},
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   {'name': 'Eindhoven', 'population': 201843},
   {'name': 'Tilburg', 'population': 193238},
   {'name': 'Groningen', 'population': 172701},
   {'name': 'Breda', 'population': 160398},
   {'name': 'Apeldoorn', 'population': 153491},
   {'name': 'Nijmegen', 'population': 152463},
   {'name': 'Enschede', 'population': 149544},
   {'name': 'Haarlem', 'population': 148772},
   {'name': 'Almere', 'population': 142465},
   {'name': 'Arnhem', 'population': 138020},
   {'name': 'Zaanstad', 'population': 135621},
   {'name': '´s-Hertogenbosch', 'population': 129170},
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   {'name': 'Ede', 'population': 101574},
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   {'name': 'Tarnów', 'population': 121494},
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   {'name': 'Kanggye', 'population': 223410},
   {'name': 'Kimchaek', 'population': 179000},
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   {'name': 'Satu Mare', 'population': 130059},
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   {'name': 'Râmnicu Vâlcea', 'population': 119741},
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   {'name': 'Târgoviste', 'population': 98980},
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   {'name': 'Târgu Jiu', 'population': 98524},
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   {'name': 'Kazan', 'population': 1101000},
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   {'name': 'Rostov-na-Donu', 'population': 1012700},
   {'name': 'Perm', 'population': 1009700},
   {'name': 'Volgograd', 'population': 993400},
   {'name': 'Voronez', 'population': 907700},
   {'name': 'Krasnojarsk', 'population': 875500},
   {'name': 'Saratov', 'population': 874000},
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   {'name': 'Uljanovsk', 'population': 667400},
   {'name': 'Izevsk', 'population': 652800},
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   {'name': 'Habarovsk', 'population': 609400},
   {'name': 'Vladivostok', 'population': 606200},
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   {'name': 'Nabereznyje Tšelny', 'population': 514700},
   {'name': 'Tula', 'population': 506100},
   {'name': 'Tjumen', 'population': 503400},
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   {'name': 'Botshabelo', 'population': 177971},
   {'name': 'Brakpan', 'population': 171363},
   {'name': 'Witbank', 'population': 167183},
   {'name': 'Oberholzer', 'population': 164367},
   {'name': 'Germiston', 'population': 164252},
   {'name': 'Springs', 'population': 162072},
   {'name': 'Westonaria', 'population': 159632},
   {'name': 'Randfontein', 'population': 120838},
   {'name': 'Paarl', 'population': 105768},
   {'name': 'Potchefstroom', 'population': 101817},
   {'name': 'Rustenburg', 'population': 97008},
   {'name': 'Nigel', 'population': 96734},
   {'name': 'George', 'population': 93818},
   {'name': 'Ladysmith', 'population': 89292}],
  'name': 'South Africa',
  'population': 40377000},
 {'cities': [{'name': 'Lusaka', 'population': 1317000},
   {'name': 'Ndola', 'population': 329200},
   {'name': 'Kitwe', 'population': 288600},
   {'name': 'Kabwe', 'population': 154300},
   {'name': 'Chingola', 'population': 142400},
   {'name': 'Mufulira', 'population': 123900},
   {'name': 'Luanshya', 'population': 118100}],
  'name': 'Zambia',
  'population': 9169000},
 {'cities': [{'name': 'Harare', 'population': 1410000},
   {'name': 'Bulawayo', 'population': 621742},
   {'name': 'Chitungwiza', 'population': 274912},
   {'name': 'Mount Darwin', 'population': 164362},
   {'name': 'Mutare', 'population': 131367},
   {'name': 'Gweru', 'population': 128037}],
  'name': 'Zimbabwe',
  'population': 11669000}]



In [89]:

    
len(country_list)









    Out[89]:





239



In [90]:

    
world = client.kipoy["world"]



In [91]:

    
world









    Out[91]:





Collection(Database(MongoClient(host=['mongodb.dobest.io:27017'], document_class=dict, tz_aware=False, connect=True), 'kipoy'), 'world')



In [92]:

    
world.insert_many(country_list)









    Out[92]:





<pymongo.results.InsertManyResult at 0xc0c4ab0>

Document 의 기준을 무엇으로 보는가? 우리는 아까 몽고DB에서 Country로 보았다

인구가 10만 이상인 도시 리스트를 뽑아올 수 있는가? 몽고 DB 쿼리 만으로는 안 된다.
왜냐하면 다큐먼트 하나하나의 기준이 country이기 때문에. 만약에 city를 기준으로 하려고 했다면 몽고 DB에서 차라리
이게 더 좋은 방법이다.



In [55]:

    
city = {
    "name": "Seoul",
    "population" = 10000000,
    "country": {
        "name": "Korea",
        "population": 50000000
    }
}









    



  File "<ipython-input-55-67c2387bc9b9>", line 3
    "population" = 10000000,
                 ^
SyntaxError: invalid syntax

어떤 잠재적 문제? city가 기준이니까 서울에서 한국이 들어가고 부산에도 들어가고
하지만 몽고 DB에서는 이게 맞는 구현 방법
그래서 중요한 것은 데이터를 잘 넣는것과 Document의 기준을 무엇으로 할 것인가? 방금 본 것은 City가 기준

관계형을 비관계형으로 바꾸어서 넣는 일은 거의 없다.
그런데 API 같은 것을 쓸 때, 어차피 우리가 테이블에 접근 못하고 받아보는 정보는 어차피 비관계형 데이터 정보를 받을 때.
직방의 경우에는 당연히 관계형 DB로 정보를 축적
하지만 우리는 API를 받아 보는 입장이기 때문에 관계를 모른다. 그러므로 몽고DB를 쓰는 것이다.
어려운 문제는 직방 1000만개의 매물을 받아왔는데 비관계형에서 관계형으로 바꾸는 것이 어렵다.
몽고 DB를 편하게 편하게 쓸 때는 그냥 받아서 데이터만 가공해서 넣어두는 것