pd.read_csv 设置 parse_date = ['column name'] 时未正确解析 date/month 字段

Question

我运行在尝试通过 pandas.read_csv() 的 parse_dates 解析少数日期时遇到了这个错误。在下面的代码片段中，我试图解析格式为 dd/mm/yy 的日期，这导致我进行了不正确的转换。在某些情况下，日期字段被视为月份，反之亦然。

为简单起见，在某些情况下 dd/mm/yy 会转换为 yyyy-dd-mm 而不是 yyyy-mm-dd。

案例一：

  04/10/96 is parsed as 1996-04-10, which is wrong.

案例二：

  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

当前输出

----------------------
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 1996-10-04
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() 来指定正确的日期格式。但就我而言，我需要转换 ['01/01/1997','01/02/1967'] 之类的日期字段很少，例如 ['//1997', '/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，而无需我编写额外的代码行。

有解决办法吗？

错误 Link @GitHub: https://github.com/pydata/pandas/issues/13063

Answer 1

在版本 pandas 0.18.0 中，您可以添加参数 dayfirst=True 然后它起作用：

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

另一个解决方案：

你可以用to_datetime with different parameters format and errors='coerce' and then combine_first解析：

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]

pd.read_csv 设置 parse_date = ['column name'] 时未正确解析 date/month 字段

pd.read_csv not correctly parsing date/month field when set parse_date = ['column name']

python

datetime

parsing

date-format

pandas