2016-03-03 25 views
1

在以下數據中,日期和時間位於不同的列中,並且我將它們梳理以獲取完整的日期時間,以使得結果列的類型爲'datetime64 [ns]'。然而有時候會有空白日期和時間的記錄,在這種情況下,結果列的類型是'object',本質上是一個字符串對象。如果所有記錄都存在,並且不存在,我該如何處理這個問題?在python pandas中清理日期和時間記錄

樣本數據

CARD,IN Date,IN Time,OUT Date,OUT Time 
100001,30-04-2015,14:19:18,01-05-2015,00:10:56 
100002,30-04-2015,11:27:52,, 
100003,30-04-2015,17:59:47,01-05-2015,04:51:52 
100004,30-04-2015,16:15:25,, 
100005,30-04-2015,10:25:13,01-05-2015,01:25:13 
100006,30-04-2015,16:59:10,, 
100007,30-04-2015,13:22:06,, 
100008,30-04-2015,09:15:29,, 
100009,30-04-2015,17:01:10,01-05-2015,01:51:01 
100010,30-04-2015,13:13:30,01-05-2015,01:37:28 
100011,30-04-2015,09:37:28,01-05-2015,00:37:28 
100012,30-04-2015,18:55:44,01-05-2015,03:22:22 
100013,30-04-2015,14:28:16,01-05-2015,01:27:18 
100014,30-04-2015,09:02:13,01-05-2015,00:02:13 
100015,30-04-2015,09:04:10,01-05-2015,00:04:10 
100016,30-04-2015,18:51:56,01-05-2015,09:51:56 
100017,30-04-2015,09:12:51,01-05-2015,00:12:51 
100018,30-04-2015,10:40:31,01-05-2015,01:40:31 
100019,30-04-2015,10:35:56,01-05-2015,01:35:56 
100020,30-04-2015,17:50:03,01-05-2015,03:54:54 
100021,30-04-2015,17:00:16,01-05-2015,02:45:35 
100022,30-04-2015,11:18:41,01-05-2015,01:15:52 

以下是我現在的代碼:

import numpy as np 
import pandas as pd 
from datetime import datetime 

#CARD,IN Date,IN Time,OUT Date,OUT Time  
data = pd.read_csv('DATA.csv', parse_dates=[['IN Date','IN Time'],['OUT Date','OUT Time'],'IN Date','OUT Date'], keep_date_col=True) 
data.rename(columns={'IN Date_IN Time':'IN','OUT Date_OUT Time':'OUT'}, inplace=True) 
data = data[['CARD','IN Date', 'IN', 'OUT Date', 'OUT']] 
#This line will fail when all the records are present 
data.ix[(data.OUT == 'nan nan'), 'OUT'] = np.nan 

enter image description here

回答

3

我想你可以TR Ÿstr.contains

data.ix[(data.OUT.str.contains('nan')), 'OUT'] = np.nan 

但最好是使用to_datetime與參數errors='coerce'

data['OUT'] = pd.to_datetime(data['OUT'], errors='coerce') 
print data 
     CARD IN Date     IN OUT Date     OUT 
0 100001 2015-04-30 2015-04-30 14:19:18 2015-01-05 2015-01-05 00:10:56 
1 100002 2015-04-30 2015-04-30 11:27:52  NaT     NaT 
2 100003 2015-04-30 2015-04-30 17:59:47 2015-01-05 2015-01-05 04:51:52 
3 100004 2015-04-30 2015-04-30 16:15:25  NaT     NaT 
4 100005 2015-04-30 2015-04-30 10:25:13 2015-01-05 2015-01-05 01:25:13 
5 100006 2015-04-30 2015-04-30 16:59:10  NaT     NaT 
6 100007 2015-04-30 2015-04-30 13:22:06  NaT     NaT 
7 100008 2015-04-30 2015-04-30 09:15:29  NaT     NaT 
8 100009 2015-04-30 2015-04-30 17:01:10 2015-01-05 2015-01-05 01:51:01 
9 100010 2015-04-30 2015-04-30 13:13:30 2015-01-05 2015-01-05 01:37:28 
10 100011 2015-04-30 2015-04-30 09:37:28 2015-01-05 2015-01-05 00:37:28 
11 100012 2015-04-30 2015-04-30 18:55:44 2015-01-05 2015-01-05 03:22:22 
12 100013 2015-04-30 2015-04-30 14:28:16 2015-01-05 2015-01-05 01:27:18 
13 100014 2015-04-30 2015-04-30 09:02:13 2015-01-05 2015-01-05 00:02:13 
14 100015 2015-04-30 2015-04-30 09:04:10 2015-01-05 2015-01-05 00:04:10 
15 100016 2015-04-30 2015-04-30 18:51:56 2015-01-05 2015-01-05 09:51:56 
16 100017 2015-04-30 2015-04-30 09:12:51 2015-01-05 2015-01-05 00:12:51 
17 100018 2015-04-30 2015-04-30 10:40:31 2015-01-05 2015-01-05 01:40:31 
18 100019 2015-04-30 2015-04-30 10:35:56 2015-01-05 2015-01-05 01:35:56 
19 100020 2015-04-30 2015-04-30 17:50:03 2015-01-05 2015-01-05 03:54:54 
20 100021 2015-04-30 2015-04-30 17:00:16 2015-01-05 2015-01-05 02:45:35 
21 100022 2015-04-30 2015-04-30 11:18:41 2015-01-05 2015-01-05 01:15:52 
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