* 겪고 있는 문제 상황을 최대한 자세하게 작성해주세요.

* 문제 해결을 위해 어떤 시도를 해보았는지 구체적으로 함께 알려주세요.

과제를 다해서 제출하긴 했는데, 결과가 산출될 때 여러 메시지가 많이 뜹니다.

어떤 부분을 손봐야 할까요?

작성한 코드 및 에러 메세지

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(',',''))
  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  

return df_result

names = list(df_listed.sample(30)['corp_name'])

dfs =[]

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

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

오류 발생 시, 작성한 코드 전체와 에러 메시지를 첨부해 주세요.

Tip 1) </> 아이콘을 눌러 코드박스를 만들어 보세요.

<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
error-케이엠에이치
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
error-디비금융스팩10호
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
error-범한퓨얼셀
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
error-써니트렌드
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
error-성일하이텍
error-연합과기공고유한공사
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
error-키움제8호스팩
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
error-신한제11호스팩
error-ISC
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)
error-베스트플로우
error-대덕GDS
error-클라스타
<ipython-input-154-b6adff02eaa8>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_result = df_result.append(doc,ignore_index=True)

	기업명	연봉(남)	연봉(여)	차이(남-여)	평균
0	다원시스	41000000	49000000	-8000000	45000000.0
0	써니전자	52340000	42117000	10223000	47228500.0
0	DN오토모티브	47781000	35902000	11879000	41841500.0
0	와이엠티	54844000	41714000	13130000	48279000.0
0	에이원알폼	44759000	31247000	13512000	38003000.0
0	경농	52084000	38208000	13876000	45146000.0
0	동원F&B	52840000	34085000	18755000	43462500.0
0	대선조선	58320000	39276000	19044000	48798000.0
0	엘브이엠씨	86869826	67585194	19284632	77227510.0
0	케이조선	42000000	22000000	20000000	32000000.0
0	에스씨디	63000000	42000000	21000000	52500000.0
0	프리엠스	59841000	37758000	22083000	48799500.0
0	아이앤씨	66000000	41000000	25000000	53500000.0
0	HMM	120946000	94797000	26149000	107871500.0
0	자화전자	55743000	28013000	27730000	41878000.0
0	엔씨소프트	118000000	82000000	36000000	100000000.0
0	아시아경제	79932000	42691000	37241000	61311500.0
0	삼성생명	135000000	90000000	45000000	112500000.0