Проверка данных и, если данные неверны, верните их или распечатайте эти записи - PullRequest
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Проверка данных и, если данные неверны, верните их или распечатайте эти записи

0 голосов
/ 11 февраля 2020

У меня есть файл данных, который имеет 14 полей (без заголовков). Я проверяю определенные поля на основе шаблона, используя метод извлечения. Я проверяю данные по столбцам.

Он проверяет данные без проблем, но единственная проблема - возвращает true / false.

Я хочу:
1. Распечатать всю строку, если в данных есть несоответствие против шаблона или
2. Напечатайте значение, которое не в правильном формате.

Я не уверен, как этого добиться.

Ниже приведен код, который я пробовал до сих пор: 

import pandas as pd
import re

try:
    #df = pd.read_csv("E:\data.txt", delimiter='\t', dtype=str, header=None)
    x= len(df.index)
    print(x)
except Exception as e:
    print (e)
try:
    df[0]= df[0].str.match('(\d\d\d\d-\d\d-\d\d)')
except Exception as e:
    print (e)

try:
    df[1]= df[1].str.match('(\d\d:\d\d:\d\d)')
except Exception as e:
    print (e)

try:        
    df[2]= df[2].str.match('(\d\d.\d\d\d\d*)')
except Exception as e:
    print (e)

try:   
    df[3]= df[3].str.match('-(\d\d.\d\d\d\d*)')
except Exception as e:
    print (e)

try:   
    df[5]= df[5].str.match('(\d\d*)')
except Exception as e:
    print (e)

try:   
    df[6]= df[6].str.match('(\d\d.\d\d\d\d*)')
except Exception as e:
    print (e)

try:   
    df[7]= df[7].str.match('(\d\d.\d\d\d\d*)')
except Exception as e:
    print (e)
print(df)

try:   
    df[13]= df[13].str.match('(\d\d*)')
except Exception as e:
    print (e)
print(df)

А ниже приведен пример набора данных: 

df = [("2018-01-01", "00:04:43", "43.71678", "-79.44384", "Tow", "53", "43.75544", "-79.43828", "C100", "1", "WL28", "CCG", "P2", "00:16:29"), 
("2018-01-01", "00:09:10", "43.78304", "-79.23663", "Lockout", "30", "43.79497", "-79.23394", "C2", "4", "WL5", "CCG", "", "00:10:05"), 
("2018-01-01", "00:15:49", "43.24116", "-79.85282", "Lockout", "134", "43.39425", "-79.98044", "H23", "9", "F109", "CCG", "", "00:48:16"), 
("2018-01-01", "00:16:47", "43.76756", "-79.41196", "Flatbed Tow", "435", "43.77409", "-79.49313", "C23", "10", "FB88", "CCG", "", "00:18:19"), 
("2018-01-01", "00:18:53", "43.26671", "-79.96222", "Tow", "172", "43.2412", "-79.85274", "H23", "11", "F109", "CCG", "", "02:42:04"), 
("2018-01-01", "00:22:59", "43.8088942", "-79.2698542", "No service", "35", "43.78196", "-79.2351", "C2", "50001", "WL5", "CLUB_AUTO", "", "00:23:04"), 
("2018-01-01", "00:25:39", "43.57866", "-79.63927", "Tow", "304", "43.59991", "-79.67094", "C950", "14", "F157", "CCG", "", "02:46:21"), 
("2018-01-01", "00:26:27", "43.72097", "-79.47553", "Lockout", "152", "43.81375", "-79.36767", "C950", "15", "F124", "CCG", "P2", "00:50:35"), 
("2018-01-01", "00:26:56", "43.785702", "-79.729198", "Jump Start/Battery Test", "55", "43.68537", "-79.80871", "C28", "50003", "FB6", "CCG", "", "00:52:26"), 
("2018-01-01", "00:28:08", "43.79901", "-79.42031", "Flatbed Tow", "67", "43.94571", "-79.44134", "C950", "50004", "F124", "CLUB_AUTO", "", "00:35:10"), 
("2018-01-01", "00:33:26", "43.67615", "-79.7707", "Tow", "84", "0", "0", "C28", "19", "FB6", "CCG", "P2", "00:54:30"), 
("2018-01-01", "00:41:30", "44.07323", "-79.48489", "Tow", "9", "44.06664", "-79.42858", "C512", "22", "FB1", "CCG", "", "00:42:50"), 
("2018-01-01", "00:43:36", "43.62484", "-79.55517", "Tow", "43", "43.68514", "-79.59623", "C16", "23", "WL1", "CCG", "P5", "00:53:31"), 
("2018-01-01", "00:43:40", "43.7088", "-79.39456", "Flat tire, with Spare", "64", "43.70485", "-79.29617", "A18", "24", "LS1", "CCG", "", "00:47:33"), 
("2018-01-01", "00:47:24", "43.87896", "-79.49169", "Tow", "96", "43.81937", "-79.56436", "C950", "26", "F157", "CCG", "", "01:12:33"), 
("2018-01-01", "00:48:17", "44.90311", "-79.43861", "Winch/Extrication", "87", "0", "0", "R130", "27", "SHOP", "CCG", "P2", "00:58:46"), 
("2018-01-01", "00:48:38", "0", "0", "Flat tire, with Spare", "72", "43.22824", "-79.77316", "G30", "50006", "WL1", "RAP_RSO", "RP", "00:54:38"), 
("2018-01-01", "00:51:39", "44.26556", "-78.35797", "No service", "55", "44.25686", "-78.28151", "P151", "30", "FB3", "CCG", "", "00:54:11"), 
("2018-01-01", "00:52:36", "43.68888", "-79.32561", "Tow", "272", "43.7969", "-79.42919", "C950", "31", "F117", "CCG", "", "04:04:21"), 
("2018-01-01", "00:53:08", "43.68968", "-79.74152", "No service", "132", "43.69461", "-79.71206", "C28", "32", "FB6", "CCG", "", "02:30:03"), 
("2018-01-01", "00:55:14", "44.2455058", "-76.9499712", "Tow", "49", "44.28096", "-76.56847", "K112M", "50007", "MD2", "CLUB_AUTO", "", "00:57:35"), 
("2018-01-01", "00:57:00", "46.31401", "-83.94554", "Winch/Extrication", "189", "46.52989", "-84.37611", "R829", "36", "FB2", "CCG", "", "01:11:20"), 
("2018-01-01", "00:58:23", "43.444523", "-80.497246", "No service", "59", "43.48807", "-80.5573", "G23", "50008", "F105", "CCG", "", "01:10:51")]

1 Ответ

0 голосов
/ 12 февраля 2020

IIU C, может быть, вы могли бы попробовать что-то вроде этого: 

dd = {1:'(\d\d:\d\d:\d\d)'
     ,2:'(\d\d.\d{4,})'
     ,3:'-(\d\d.\d{4,})'
     ,5:'(\d\d*)'}

for i, j in dd.items():
    print(f'Checking column {i}')
    print(df[~df[i].astype(str).str.match(j)])
...