1. Sample JSON Site : https://api.androidhive.info/contacts/

2. Import Module

import requests
import json
from pandas import DataFrame
from print_df import print_df

3. Sample Code

import requests
import json
from pandas import DataFrame
from print_df import print_df


url = 'https://api.androidhive.info/contacts'

response = requests.get(url)

if response.status_code != 200:
 print("[%d Error] %s" % (response.status_code, response.reason))
 quit()

response.encoding = 'UTF-8'

result = json.loads(response.text)

df = DataFrame(result['contacts'])

name = []

for i in result['contacts']:
 name.append(i['name'])

name_dict = {}
for i, v in enumerate(name):
 name_dict[i] = v

df.rename(index=name_dict, inplace=True)
df.drop('name', axis=1, inplace=True)

print_df(df)

<class 'pandas.core.frame.DataFrame'>

(13, 5)

+-------------------+------------------------------------+-----------------------------+--------+-------+--------------------------------------------------------------------------+

| | address | email | gender | id | phone |

+-------------------+------------------------------------+-----------------------------+--------+-------+--------------------------------------------------------------------------+

| Ravi Tamada | xx-xx-xxxx,x - street, x - country | ravi@gmail.com | male | c200 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Johnny Depp | xx-xx-xxxx,x - street, x - country | johnny_depp@gmail.com | male | c201 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Leonardo Dicaprio | xx-xx-xxxx,x - street, x - country | leonardo_dicaprio@gmail.com | male | c202 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| John Wayne | xx-xx-xxxx,x - street, x - country | john_wayne@gmail.com | male | c203 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Angelina Jolie | xx-xx-xxxx,x - street, x - country | angelina_jolie@gmail.com | female | c204 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Dido | xx-xx-xxxx,x - street, x - country | dido@gmail.com | female | c205 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Adele | xx-xx-xxxx,x - street, x - country | adele@gmail.com | female | c206 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Hugh Jackman | xx-xx-xxxx,x - street, x - country | hugh_jackman@gmail.com | male | c207 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Will Smith | xx-xx-xxxx,x - street, x - country | will_smith@gmail.com | male | c208 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Clint Eastwood | xx-xx-xxxx,x - street, x - country | clint_eastwood@gmail.com | male | c209 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Barack Obama | xx-xx-xxxx,x - street, x - country | barack_obama@gmail.com | male | c2010 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Kate Winslet | xx-xx-xxxx,x - street, x - country | kate_winslet@gmail.com | female | c2011 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

| Eminem | xx-xx-xxxx,x - street, x - country | eminem@gmail.com | male | c2012 | {'mobile': '+91 0000000000', 'home': '00 000000', 'office': '00 000000'} |

+-------------------+------------------------------------+-----------------------------+--------+-------+--------------------------------------------------------------------------+

Process finished with exit code 0

4. Sample Code 풀이

- 데이터 프레임으로 변환 작업

df = DataFrame(result['contacts'])

- name을 인덱스 대신 사용하기 위해 리스트 형식으로 추출

name = []

for i in result['contacts']:
 name.append(i['name'])

- name 딕셔너리 생성

name_dict = {}
for i, v in enumerate(name):
 name_dict[i] = v

- Data Frame의 인덱스 변경 및 name 컬럼 삭제

df.rename(index=name_dict, inplace=True)
df.drop('name', axis=1, inplace=True)

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