我试图通过pandas.read_csv()的parse_dates解析几个日期时遇到了这个bug.在下面的代码片段中,我试图解析格式为dd / mm / yy的日期,这导致我转换不正确.在某些情况下,日期字段被视为月份,反之亦然.
为了简单起见,在某些情况下,dd / mm / yy会转换为yyyy-dd-mm而不是yyyy-mm-dd.
情况1：
04/10/96 is parsed as 1996-04-10,which is wrong.
案例2：
15/07/97 is parsed as 1997-07-15,which is correct.
案例3：
10/12/97 is parsed as 1997-10-12,which is wrong.
代码示例
import pandas as pd
df = pd.read_csv('date_time.csv')
print 'Data in csv:'
print df
print df['start_date'].dtypes
print '----------------------------------------------'
df = pd.read_csv('date_time.csv',parse_dates = ['start_date'])
print 'Data after parsing:'
print df
print df['start_date'].dtypes
电流输出
----------------------
Data in csv:
----------------------
start_date
0 04/10/96
1 15/07/97
2 10/12/97
3 06/03/99
4 //1994
5 /02/1967
object
----------------------
Data after parsing:
----------------------
start_date
0 1996-04-10
1 1997-07-15
2 1997-10-12
3 1999-06-03
4 1994-01-01
5 1967-02-01
datetime64[ns]
预期产出
----------------------
Data in csv:
----------------------
start_date
0 04/10/96
1 15/07/97
2 10/12/97
3 06/03/99
4 //1994
5 /02/1967
object
----------------------
Data after parsing:
----------------------
start_date
0 1996-10-04
1 1997-07-15
2 1997-12-10
3 1999-03-06
4 1994-01-01
5 1967-02-01
datetime64[ns]
更多评论：
我可以使用date_parser或pandas.to_datetime()来指定日期的正确格式.但在我的情况下,我有几个日期字段,如[‘// 1997′,’/ 02/1967′]我需要转换[’01 / 01/1997′,’01/02/1967’]. parse_dates帮助我将这些类型的日期字段转换为预期的格式,而不会让我编写额外的代码行.
这有什么解决方案吗？
Bug Link @GitHub：https://github.com/pydata/pandas/issues/13063
在版本pandas 0.18.0中,您可以添加参数dayfirst = True然后它可以工作：
import pandas as pd
import io
temp=u"""start_date
04/10/96
15/07/97
10/12/97
06/03/99
//1994
/02/1967
"""
#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp),parse_dates = ['start_date'],dayfirst=True)
start_date
0 1996-10-04
1 1997-07-15
2 1997-12-10
3 1999-03-06
4 1994-01-01
5 1967-02-01
另一种方案：
你可以用to_datetime解析不同的参数格式和错误=’coerce’然后combine_first：
date1 = pd.to_datetime(df['start_date'],format='%d/%m/%y',errors='coerce')
print date1
0 1996-10-04
1 1997-07-15
2 1997-12-10
3 1999-03-06
4 NaT
5 NaT
Name: start_date,dtype: datetime64[ns]
date2 = pd.to_datetime(df['start_date'],format='/%m/%Y',errors='coerce')
print date2
0 NaT
1 NaT
2 NaT
3 NaT
4 NaT
5 1967-02-01
Name: start_date,dtype: datetime64[ns]
date3 = pd.to_datetime(df['start_date'],format='//%Y',errors='coerce')
print date3
0 NaT
1 NaT
2 NaT
3 NaT
4 1994-01-01
5 NaT
Name: start_date,dtype: datetime64[ns]
print date1.combine_first(date2).combine_first(date3)
0 1996-10-04
1 1997-07-15
2 1997-12-10
3 1999-03-06
4 1994-01-01
5 1967-02-01
Name: start_date,dtype: datetime64[ns]
