금융인을 위한 파이썬 업무자동화 3주차

스파르타코딩클럽 금융인을 위한 파이썬 업무자동화 3주차

 

[다트 open api 발급]

https://opendart.fss.or.kr/uat/uia/egovLoginUsr.do

전자공시 OPENDART 시스템 | 로그인

 

[ 구글 드라이브 코랩 가기]

https://drive.google.com/

Google Drive: 로그인

 

[ dart-fss 라이브러리 설치]

!pip install dart-fss

 

 

 

[ 키 넣고, 구동해보기] 

 

import dart_fss as dart_fss
import pandas as pd

api_key = '여기에 API 키를 입력'
dart_fss.set_api_key(api_key=api_key)

corp_list = dart_fss.get_corp_list()

corp_list.corps

 

 

[ dart 공식 문서] 

https://dart-fss.readthedocs.io/en/latest/

DART-FSS — dart-fss documentation v0.3.10 documentation

 

[ dart 상장/비상장 excel 추출] 

import dart_fss as dart_fss
import pandas as pd

api_key = 'a4f6fad903b1302a1c54b6d63d2c0f7f5fc26838'
dart_fss.set_api_key(api_key=api_key)

all = dart_fss.api.filings.get_corp_code()
df = pd.DataFrame(all)

df_listed = df[df['stock_code'].notnull()]
# 비상장종목
df_non_listed = df[df['stock_code'].isnull()]
# 종목 갯수
#df_listed.count() 

df_listed.to_excel('상장종목.xlsx')
df_non_listed.to_excel('비상장종목.xlsx')

[ dart API 사용해보기] 

import dart_fss as dart_fss
import pandas as pd

api_key = 'a4f6fad903b1302a1c54b6d63d2c0f7f5fc26838'
dart_fss.set_api_key(api_key=api_key)
all = dart_fss.api.filings.get_corp_code()
df = pd.DataFrame(all)

df_listed = df[df['stock_code'].notnull()]
# 비상장종목
df_non_listed = df[df['stock_code'].isnull()]
corp_code = df_listed[df_listed['corp_name']== '카카오'].iloc[0,0]
# 기업개황
#dart_fss.api.filings.get_corp_info(corp_code)
# 사업보고서
#data =dart_fss.api.info.unrst_exctv_mendng_sttus(corp_code, '2022', '11011')
# 증자 현황
#data = dart_fss.api.info.irds_sttus(corp_code, '2022', '11011', api_key=None)
# 배당 현황
#data =dart_fss.api.info.alot_matter(corp_code, '2022', '11011', api_key=None)
# 최대주주 변동현황
#data =dart_fss.api.info.hyslr_chg_sttus(corp_code,  '2022',   '11011', api_key=None)
# 최대 주주현황
#data =dart_fss.api.info.hyslr_sttus(corp_code,  '2022',   '11011', api_key=None)
#pd.DataFrame(data['list'])[['nm','bsis_posesn_stock_qota_rt','trmend_posesn_stock_qota_rt']]
# 임원사항
#data = dart_fss.api.info.exctv_sttus(corp_code, '2022', '11011')
# 직원정보
#data = dart_fss.api.info.emp_sttus(corp_code, '2022', '11011')
# 이사보수
#data = dart_fss.api.info.hmv_audit_indvdl_by_sttus(corp_code, '2022', '11011')
# 연봉TOP5
#data = dart_fss.api.info.indvdl_by_pay(corp_code, '2022', '11011')
# 타법인 출자현황
#data = dart_fss.api.info.otr_cpr_invstmnt_sttus(corp_code, '2022', '11011')
# 상장기업 재무정보
#data = dart_fss.api.finance.fnltt_singl_acnt(corp_code, '2022', '11011')
# 상장기업 재무 모든 정보
#data = dart_fss.api.finance.fnltt_singl_acnt_all(corp_code, '2021', '11011', 'CFS')
#임원, 주요 주주 소유 보고
#data = dart_fss.api.shareholder.elestock(corp_code)
# 특정 사람
#df_temp = pd.DataFrame(data['list'])
#df_temp[df_temp['repror'] == '김범수']

#pd.DataFrame(data['list'])

 

[ 다트 API로 연봉 킹 30명 찾기] 

import dart_fss as dart_fss
import pandas as pd

api_key = 'a4f6fad903b1302a1c54b6d63d2c0f7f5fc26838'
dart_fss.set_api_key(api_key=api_key)
all = dart_fss.api.filings.get_corp_code()
df = pd.DataFrame(all)

df_listed = df[df['stock_code'].notnull()]
# 비상장종목
df_non_listed = df[df['stock_code'].isnull()]

def get_salary(name):
  corp_code = df_listed[df_listed['corp_name']== name].iloc[0,0]
  data = dart_fss.api.info.indvdl_by_pay(corp_code, '2022', '11011', api_key=None)

  df = pd.DataFrame(data['list'])

  df = df[['corp_name','nm','ofcps','mendng_totamt']]

  df.columns = ['기업명','이름','역할','보수']
  df['보수'] = pd.to_numeric(df['보수'].str.replace(',',''))
  #df.sort_values(by='보수',ascending=False)
  return df

names = ['삼성전자','LG에너지솔루션','SK하이닉스','NAVER','삼성바이오로직스','삼성전자우','카카오','삼성SDI','현대차','LG화학','기아','POSCO홀딩스','KB금융','카카오뱅크','셀트리온','신한지주','삼성물산','현대모비스','SK이노베이션','LG전자','카카오페이','SK','한국전력','크래프톤','하나금융지주','LG생활건강','HMM','삼성생명','하이브','두산중공업','SK텔레콤','삼성전기','SK바이오사이언스','LG','S-Oil','고려아연','KT&G','우리금융지주','대한항공','삼성에스디에스','현대중공업','엔씨소프트','삼성화재','아모레퍼시픽','KT','포스코케미칼','넷마블','SK아이이테크놀로지','LG이노텍','기업은행']

dfs =[]
for name in names:
  try:
    df = get_salary(name)
    dfs.append(df)
  except:
    print(f'error- {name}')  

df_result = pd.concat(dfs)
df_result.sort_values(by='보수',ascending=False).head(30)

 

[ 상장종목 10개 샘플 지분율 구하기] 

import dart_fss as dart_fss
import pandas as pd

api_key = 'a4f6fad903b1302a1c54b6d63d2c0f7f5fc26838'
dart_fss.set_api_key(api_key=api_key)
all = dart_fss.api.filings.get_corp_code()
df = pd.DataFrame(all)

df_listed = df[df['stock_code'].notnull()]
# 비상장종목
df_non_listed = df[df['stock_code'].isnull()]


def get_shareholders(corp_code):

  #corp_code = df_listed[df_listed['corp_name']== '카카오'].iloc[0,0]
  data = dart_fss.api.info.hyslr_sttus(corp_code, '2022', '11011', api_key=None)

  df = pd.DataFrame(data['list'])
  df= df[['corp_name','nm','relate','bsis_posesn_stock_qota_rt','trmend_posesn_stock_qota_rt','rm']]
  df.columns = ['기업명','이름','관계','기초지분율','기말지분율','비고']
  df = df[df['관계'].notnull()]
  df['기초지분율'] = pd.to_numeric(df['기초지분율'])
  df['기말지분율'] = pd.to_numeric(df['기말지분율'])
  return df.sort_values(by='기초지분율',ascending=False).head(3)

corp_codes = list(df_listed.sample(10)['corp_code'])

dfs =[]
for corp_code in corp_codes:
  try:
    df = get_shareholders(corp_code)
    dfs.append(df)
  except:
    print(f'error-{corp_code}')

df_result = pd.concat(dfs)
df_result['증감'] = df_result['기말지분율'] - df_result['기초지분율']
df_result.sort_values(by='증감',ascending=False)

 

[ 이익잉영금 증가 10개 샘플 기업 구하기] 

import dart_fss as dart_fss
import pandas as pd

api_key = 'a4f6fad903b1302a1c54b6d63d2c0f7f5fc26838'
dart_fss.set_api_key(api_key=api_key)
all = dart_fss.api.filings.get_corp_code()
df = pd.DataFrame(all)

df_listed = df[df['stock_code'].notnull()]
# 비상장종목
df_non_listed = df[df['stock_code'].isnull()]

def get_profit(name):
  corp_code = df_listed[df_listed['corp_name'] == name].iloc[0,0]
  data = dart_fss.api.finance.fnltt_singl_acnt(corp_code, '2022', '11011')
  df= pd.DataFrame(data['list'])


  # 연결재무제표 CFS , 이익잉영금
  cond = (df['fs_div'] =='CFS') & (df['account_nm'] =='이익잉여금')

  df = df[cond]

  df['name'] = name

  # 전기, 이번기
  df = df[['name','thstrm_amount','frmtrm_amount']]

  df.columns = ['기업명','당기','전기']

  df['당기'] = pd.to_numeric(df['당기'].str.replace(',',''))
  df['전기'] = pd.to_numeric(df['전기'].str.replace(',',''))

  df['증감'] = df['당기'] - df['전기']
  df['증감율'] = abs(df['당기']) / abs(df['전기'])

  return df

names =df_listed.sample(10)['corp_name']

dfs =[]
for name in names:
  try:
    df = get_profit(name)
    dfs.append(df)
  except:
    print(f'error - {name}')

df_result = pd.concat(dfs)
df_result.sort_values(by='증감율', ascending=False)

[ 비상장종목 당기순이 10개 샘플 기업 구하기] 

import dart_fss as dart_fss
import pandas as pd

api_key = 'a4f6fad903b1302a1c54b6d63d2c0f7f5fc26838'
dart_fss.set_api_key(api_key=api_key)
all = dart_fss.api.filings.get_corp_code()
df = pd.DataFrame(all)

# 비상장종목
df_non_listed = df[df['stock_code'].isnull()]
def get_earning(name):
  corp_code = df_non_listed[df_non_listed['corp_name'] == name].iloc[0,0]
  data = dart_fss.api.info.alot_matter(corp_code, '2021', '11011')

  df= pd.DataFrame(data['list'])

  df = df[df['se'] =='(연결)당기순이익(백만원)']
  df = df[['corp_name','thstrm','frmtrm','lwfr']]

  df.columns = ['기업명','2021','2020','2019']

  df['2021'] = pd.to_numeric(df['2021'].str.replace(',',''))
  df['2020'] = pd.to_numeric(df['2020'].str.replace(',',''))
  df['2019'] = pd.to_numeric(df['2019'].str.replace(',',''))

  return df 

names = list(df_non_listed.sample(10)['corp_name'])

dfs =[]
for name in names:
  try:
    df =get_earning(name)  
    dfs.append(df)
  except:
    print(f'error - {name} ')

result_df = pd.concat(dfs)
result_df

 

[ 상장종목 10개 샘플 뽑아 그 중 남녀 급여차이 적은 기업  구하기] 

import dart_fss as dart_fss
import pandas as pd

api_key = 'a4f6fad903b1302a1c54b6d63d2c0f7f5fc26838'
dart_fss.set_api_key(api_key=api_key)
all = dart_fss.api.filings.get_corp_code()
df = pd.DataFrame(all)

df_listed = df[df['stock_code'].notnull()]
# 비상장종목
df_non_listed = df[df['stock_code'].isnull()]

def get_salary(name):
  corp_code = df_listed[df_listed['corp_name'] == name].iloc[0,0]
  data = dart_fss.api.info.emp_sttus(corp_code, '2021', '11011')

  df =pd.DataFrame(data['list']) 
  df = df[['corp_name','sexdstn','jan_salary_am']]
  df_result = pd.DataFrame()
  doc = {
      '기업명'   : name,
      '연봉(남)' : df[df['sexdstn']=='남'].iloc[-1,-1],
      '연봉(여)' : df[df['sexdstn']=='여'].iloc[-1,-1]
  }
  df_result = df_result.append(doc,ignore_index=True)

  df_result['연봉(남)'] = pd.to_numeric(df_result['연봉(남)'].str.replace(',',''))
  df_result['연봉(여)'] = pd.to_numeric(df_result['연봉(여)'].str.replace(',',''))
      
  return df_result

names = list(df_listed.sample(10)['corp_name'])

dfs =[]
for name in names:
  try:
    df = get_salary(name)
    dfs.append(df)
  except:
    print(f'error- {name}')

df_result = pd.concat(dfs)

df_result['차이(남-여)'] = df_result['연봉(남)'] - df_result['연봉(여)']
df_result['평균'] = (df_result['연봉(남)'] + df_result['연봉(여)'])/2

df_result.sort_values(by='차이(남-여)', ascending=True)

 

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