쉼표로 팬더에서 CSV 파일 구문 분석

Question

csv 파일로 pandas.DataFrame을 만들어야합니다. 이를 위해 pandas.csv_reader(...) 메서드를 사용하고 있습니다. 이 파일의 문제점은 하나 이상의 열에 값 (파일 형식을 제어하지 않음) 내에 쉼표가 포함되어 있다는 것입니다. 나는이 question에서 솔루션을 구현하기 위해 노력하고 있지만 다음과 같은 오류 얻을 : 나는 정부가 비어 시도 csv 파일이 솔루션을 구현 한 후 몇 가지 이유를 들어

pandas.errors.EmptyDataError: No columns to parse from file 

합니다. 여기

나는 코드를 사용하고 있습니다 :

# fix csv file 
with open ("/Users/username/works/test.csv",'rb') as f,\ 
open("/Users/username/works/test.csv",'wb') as g: 
    writer = csv.writer(g, delimiter=',') 
    for line in f: 
     row = line.split(',', 4) 
     writer.writerow(row) 
# Manipulate csv file 
data = pd.read_csv(os.path.expanduser\ 
("/Users/username/works/test.csv"),error_bad_lines=False) 

어떤 아이디어?

데이터 개요 :

Id0 Id 1 Id 2 Country Company Title  Email     
    23 123  456 AR  name cargador email@email.com     

    24 123  456 AR  name Executive assistant email@email.com     

    25 123  456 AR  name Asistente Administrativo email@email.com     

    26 123  456 AR  name Atención al cliente vía telefónica vía online email@email.com    
    39 123  456 AR  name Asesor de ventas email@email.com     

    40 123  456 AR  name inc. International company representative email@email.com    
    41 123  456 AR  name Vendedor de campo email@email.com     

    42 123  456 AR  name PUBLICIDAD ATENCIÓN AL CLIENTE email@email.com    
    43 123  456 AR  name Asistente de Marketing email@email.com     

    44 123  456 AR  name SOLDADOR email@email.com     
    217 123  456 AR  name Se requiere vendedores  Loja Quevedo  Guayas) email@email.com 
    218 123  456 AR  name Ing. Civil recién graduado Yaruquí email@email.com    
219 123  456 AR  name ayudantes enfermeria email@email.com     

220 123  456 AR  name Trip Leader for International Youth Exchange email@email.com     
221 123  456 AR  name COUNTRY MANAGER/DIRECTOR COMERCIAL email@email.com     
250 123  456 AR  name Ayudante de Pasteleria email@email.com Asesor email@email.com email@email.com  

사전 분석 CSV :

#,Id 1,Id 2,Country,Company,Title,Email,,,, 
23,123,456,AR,name,cargador,email@email.com,,,, 
24,123,456,AR,name,Executive assistant,email@email.com,,,, 
25,123,456,AR,name,Asistente Administrativo,email@email.com,,,, 
26,123,456,AR,name,Atención al cliente vía telefónica , vía online,email@email.com,,, 
39,123,456,AR,name,Asesor de ventas,email@email.com,,,, 
40,123,456,AR,name, inc.,International company representative,email@email.com,,, 
41,123,456,AR,name,Vendedor de campo,email@email.com,,,, 
42,123,456,AR,name,PUBLICIDAD, ATENCIÓN AL CLIENTE,email@email.com,,, 
43,123,456,AR,name,Asistente de Marketing,email@email.com,,,, 
44,123,456,AR,name,SOLDADOR,email@email.com,,,, 
217,123,456,AR,name,Se requiere vendedores,, Loja , Quevedo, Guayas),email@email.com 
218,123,456,AR,name,Ing. Civil recién graduado, Yaruquí,email@email.com,,, 
219,123,456,AR,name,ayudantes enfermeria,email@email.com,,,, 
220,123,456,AR,name,Trip Leader for International Youth Exchange,email@email.com,,,, 
221,123,456,AR,name,COUNTRY MANAGER/DIRECTOR COMERCIAL,email@email.com,,,, 
250,123,456,AR,name,Ayudante de Pasteleria,email@email.com, Asesor,email@email.com,email@email.com, 
251,123,456,AR,name,Ejecutiva de Ventas,email@email.com,,,, 

Answer 1

당신은 어떤 쉼표가 공간 다음에, 그 모든 것을의의 comapny에 대한 것을 가정 할 경우 나머지 쉼표가 전자 메일 주소보다 먼저 열에 있으면 작은 구문 분석기가이를 처리 할 수 ​​있습니다.

코드 :

import csv 
import re 

VALID_EMAIL = re.compile(r'[^@]+@[^@]+\.[^@]+') 

def read_my_csv(file_handle): 
    # build csv reader 
    reader = csv.reader(file_handle) 

    # get the header, and find the e-mail and title columns 
    header = next(reader) 
    email_column = header.index('Email') 
    title_column = header.index('Title') 

    # yield the header up to the e-mail column 
    yield header[:email_column+1] 

    # for each row, go through rebuild columns 
    for row in reader: 

     # for each row, put the Company column back together 
     while row[title_column].startswith(' '): 
      row[title_column-1] += ',' + row[title_column] 
      del row[title_column] 

     # for each row, put the Title column back together 
     while not VALID_EMAIL.match(row[email_column]): 
      row[email_column-1] += ',' + row[email_column] 
      del row[email_column] 
     yield row[:email_column+1] 

시험 코드 :

with open ("test.csv", 'rU') as f: 
    generator = read_my_csv(f) 
    columns = next(generator) 
    df = pd.DataFrame(generator, columns=columns) 

print(df) 

결과 :

 # Id 1 Id 2 Country  Company \ 
0 23 123 456  AR  name 
1 24 123 456  AR  name 
2 25 123 456  AR  name 
3 26 123 456  AR  name 
4 39 123 456  AR  name 
5 40 123 456  AR name, inc. 
6 41 123 456  AR  name 
7 42 123 456  AR  name 
8 43 123 456  AR  name 
9 44 123 456  AR  name 
10 217 123 456  AR  name 
11 218 123 456  AR  name 
12 219 123 456  AR  name 
13 220 123 456  AR  name 
14 221 123 456  AR  name 
15 250 123 456  AR  name 
16 251 123 456  AR  name 

               Title   Email 
0           cargador email@email.com 
1        Executive assistant email@email.com 
2       Asistente Administrativo email@email.com 
3 Atención al cliente vía telefónica , vía online email@email.com 
4         Asesor de ventas email@email.com 
5    International company representative email@email.com 
6         Vendedor de campo email@email.com 
7     PUBLICIDAD, ATENCIÓN AL CLIENTE email@email.com 
8        Asistente de Marketing email@email.com 
9           SOLDADOR email@email.com 
10 Se requiere vendedores,, Loja , Quevedo, Guayas) email@email.com 
11    Ing. Civil recién graduado, Yaruquí email@email.com 
12        ayudantes enfermeria email@email.com 
13  Trip Leader for International Youth Exchange email@email.com 
14    COUNTRY MANAGER/DIRECTOR COMERCIAL email@email.com 
15       Ayudante de Pasteleria email@email.com 
16        Ejecutiva de Ventas email@email.com