pd.read_csv在設置parse_date = ['列名']時沒有正確解析日期/月份字段

Question

我嘗試通過pandas.read_csv()的parse_dates解析幾個日期時遇到了這個錯誤。在下面的代碼片段中，我試圖解析格式爲dd/mm/yy的日期，這導致我進行了不正確的轉換。對於某些情況，日期字段被視爲月份，反之亦然。

爲了保持簡單，在某些情況下dd/mm/yy得到轉換爲yyyy-dd-mm而不是yyyy-mm-dd。

情況1：

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

情況2：

15/07/97 is parsed as 1997-07-15, which is correct. 

情況3：

10/12/97 is parsed as 1997-10-12, which is wrong. 

代碼示例

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 

篇電流輸出

---------------------- 
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] 

期望輸出

---------------------- 
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] 

更多評論：

我可以使用date_parser或pandas.to_datetime()指定日期的正確格式。但在我的情況下，我有幾個日期字段，如['//1997', '/02/1967']，我需要將其轉換爲['01/01/1997','01/02/1967']。 parse_dates可以幫助我將這些類型的日期字段轉換爲預期的格式，而無需編寫額外的代碼行。

有沒有解決方案？

錯誤鏈接@GitHub：https://github.com/pydata/pandas/issues/13063

Answer 1

在版本大熊貓0.18.0您可以添加參數dayfirst=True，然後它的工作原理：

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 

另一種解決方案：

您可以to_datetime用不同的參數解析format和errors='coerce'然後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]