Question

我有一个pandas数据框，其中多列字符串代表日期，空字符串代表缺少日期。例如

import numpy as np
import pandas as pd

# expected date format is 'm/%d/%Y'

custId = np.array(list(range(1,6)))
eventDate = np.array(["06/10/1992","08/24/2012","04/24/2015","","10/14/2009"])
registerDate = np.array(["06/08/2002","08/20/2012","04/20/2015","","10/10/2009"])

# both date columns of dfGood should convert to datetime without error
dfGood = pd.DataFrame({'custId':custId, 'eventDate':eventDate, 'registerDate':registerDate})

我想：

有效地将所有字符串都是有效日期的列或空白转换为datetime64类型的列（空NaT）
当任何非空字符串不符合预期格式时，提升ValueError

应该引发ValueError的示例：

# 2nd string invalid
registerDate = np.array(["06/08/2002","20/08/2012","04/20/2015","","10/10/2009"]) 
# eventDate column should convert, registerDate column should raise ValueError
dfBad = pd.DataFrame({'custId':custId, 'eventDate':eventDate, 'registerDate':registerDate})

此功能在元素级别执行我想要的操作：

from datetime import datetime

def parseStrToDt(s, format = '%m/%d/%Y'):
    """Parse a string to datetime with the supplied format."""
    return pd.NaT if s=='' else datetime.strptime(s, format)

print(parseStrToDt("")) # correctly returns NaT
print(parseStrToDt("12/31/2011")) # correctly returns 2011-12-31 00:00:00
print(parseStrToDt("12/31/11")) # correctly raises ValueError

但是，我read字符串操作不应该是np.vectorize - d。我认为这可以使用pandas.DataFrame.apply有效地完成，如：

dfGood[['eventDate','registerDate']].applymap(lambda s: parseStrToDt(s)) # raises TypeError

dfGood.loc[:,'eventDate'].apply(lambda s: parseStrToDt(s)) # raises same TypeError

我猜测TypeError与我的函数有关，返回不同的dtype，但我确实想利用动态类型并用日期时间替换字符串（除非引发ValueError）...所以我怎么能这样做？

Answer 1

pandas没有完全复制你想要的选项，这是一种方法，这应该是相对有效的。

In [4]: dfBad
Out[4]: 
   custId   eventDate registerDate
0       1  06/10/1992   06/08/2002
1       2  08/24/2012   20/08/2012
2       3  04/24/2015   04/20/2015
3       4                         
4       5  10/14/2009   10/10/2009

In [7]: cols
Out[7]: ['eventDate', 'registerDate']

In [9]: dts = dfBad[cols].apply(lambda x: pd.to_datetime(x, errors='coerce', format='%m/%d/%Y'))

In [10]: dts
Out[10]: 
   eventDate registerDate
0 1992-06-10   2002-06-08
1 2012-08-24          NaT
2 2015-04-24   2015-04-20
3        NaT          NaT
4 2009-10-14   2009-10-10

In [11]: mask = pd.isnull(dts) & (dfBad[cols] != '')

In [12]: mask
Out[12]: 
  eventDate registerDate
0     False        False
1     False         True
2     False        False
3     False        False
4     False        False


In [13]: mask.any()
Out[13]: 
eventDate       False
registerDate     True
dtype: bool

In [14]: is_bad = mask.any()

In [23]: if is_bad.any():
    ...:     raise ValueError("bad dates in col(s) {0}".format(is_bad[is_bad].index.tolist()))
    ...: else:
    ...:     df[cols] = dts
    ...:     
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-23-579c06ce3c77> in <module>()
      1 if is_bad.any():
----> 2     raise ValueError("bad dates in col(s) {0}".format(is_bad[is_bad].index.tolist()))
      3 else:
      4     df[cols] = dts
      5 

ValueError: bad dates in col(s) ['registerDate']

Answer 2

为了进一步接受已接受的答案，我用解析的日期时间替换了所有有效或缺失字符串的列，然后为剩余的未解析列引发了错误：

dtCols = ['eventDate', 'registerDate']
dts = dfBad[dtCols].apply(lambda x: pd.to_datetime(x, errors='coerce', format='%m/%d/%Y'))

mask = pd.isnull(dts) & (dfBad[dtCols] != '')
colHasError = mask.any()

invalidCols = colHasError[colHasError].index.tolist() 
validCols = list(set(dtCols) - set(invalidCols))

dfBad[validCols] = dts[validCols] # replace the completely valid/empty string cols with dates
if colHasError.any():
    raise ValueError("bad dates in col(s) {0}".format(invalidCols))
# raises:  ValueError: bad dates in col(s) ['registerDate']

print(dfBad) # eventDate got converted, registerDate didn't

接受的答案包含主要的见解，即继续向NaT强制执行错误，然后使用掩码区分非空但无效的字符串与空字符串。

pandas将字符串列转换为datetime，允许丢失但不是无效

2 个答案: