朱利安与格雷戈里安日期与OutOfBoundsDatetime

时间:2017-11-15 10:58:19

标签: python pandas datetime

我正在使用pandas数据框,我有一个包含julian日期的DATE列。我想将该列的每个值转换为格里高利日期。

为了达到这个目的,我使用了以下代码:

df[['DATE']] = df[['DATE']].apply(lambda x: pd.to_datetime(x - pd.Timestamp(0).to_julian_date(), unit='D'))

不幸的是,我收到的错误如下:

OutOfBoundsDatetime: ("cannot convert input 381088.5 with the unit 'D'", u'occurred at index MXPLD_DATE')

当我查找导致数据框出现问题的输入值时,它根本不存在,我不知道381088.5来自何处。

你能告诉我我做错了吗?

谢谢。

编辑1

我尝试了@jezrael解决方案,但我仍然遇到了类似的错误。

df['DATE'] = pd.to_datetime(df['DATE'], unit='D', origin='julian')

错误:

---------------------------------------------------------------------------
OutOfBoundsDatetime                       Traceback (most recent call last)
<ipython-input-18-4353e2be1ced> in <module>()
----> 1 df['DATE'] = pd.to_datetime(df['DATE'], unit='D', origin='julian')

/opt/anaconda2/lib/python2.7/site-packages/pandas/core/tools/datetimes.pyc in to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin)
    469             raise tslib.OutOfBoundsDatetime(
    470                 "{original} is Out of Bounds for "
--> 471                 "origin='julian'".format(original=original))
    472 
    473     elif origin not in ['unix', 'julian']:

OutOfBoundsDatetime: 0          2457184
1          2457155
2          2457155
3          2457155
4          2457155
5          2457155
6          2457155
7          2457155
8          2457155
9          2457155
10         2457155
11         2457155
12         2457155
13         2457155
14         2457155
15         2457155
16         2457155
17         2457155
18         2457155
19         2457155
20         2457155
21         2457155
22         2457155
23         2457155
24         2457155
25         2457155
26         2457155
27         2457155
28         2457701
29         2457701
            ...   
4597928    2457724
4597929    2457724
4597930    2457724
4597931    2457724
4597932    2457724
4597933    2457724
4597934    2457724
4597935    2457724
4597936    2457724
4597937    2457724
4597938    2457724
4597939    2457724
4597940    2457724
4597941    2457724
4597942    2457724
4597943    2457724
4597944    2457724
4597945    2457724
4597946    2457724
4597947    2457724
4597948    2457724
4597949    2457724
4597950    2457724
4597951    2457724
4597952    2457724
4597953    2457724
4597954    2457724
4597955    2457724
4597956    2457724
4597957    2457724
Name: DATE, Length: 4597958, dtype: int64 is Out of Bounds for origin='julian'

1 个答案:

答案 0 :(得分:2)

我认为您需要to_datetime参数origin

df = pd.DataFrame({'julian':[2458072.5, 2458073.5]})

df['date'] = pd.to_datetime(df['julian'], unit='D', origin='julian')
print (df)
      julian       date
0  2458072.5 2017-11-15
1  2458073.5 2017-11-16

编辑:

某些日期时间OutOfBounds存在问题。

首先检查timestamp limitations

In [66]: pd.Timestamp.min
Out[66]: Timestamp('1677-09-21 00:12:43.145225')

In [67]: pd.Timestamp.max
Out[67]: Timestamp('2262-04-11 23:47:16.854775807')

然后获得最小的朱利安日期时间(通过在线转换,例如here):

maxdate = 2547338
mindate = 2333836

然后为超出范围的日期添加NaN,例如where

 df = pd.DataFrame({'julian':[2821676, 2547338, 1, 2333836]})
maxdate = 2547338
mindate = 2333836

clean_dates = df['julian'].where(df['julian'].between(mindate, maxdate))
print (clean_dates)
0          NaN
1    2547338.0
2          NaN
3    2333836.0

df['date'] = pd.to_datetime(clean_dates, unit='D', origin='julian')
print (df)
    julian                date
0  2821676                 NaT
1  2547338 2262-04-10 12:00:00
2        1                 NaT
3  2333836 1677-09-21 12:00:00

最后为您的数据应用解决方案 - 有2个值转换为NaT

print (df['MXPLD_DATE'][~df['MXPLD_DATE'].between(mindate, maxdate)])

1217806    2821676
3167148    2821676
Name: MXPLD_DATE, dtype: int64

clean_dates = df['MXPLD_DATE'].where(df['MXPLD_DATE'].between(mindate, maxdate))        
df['MXPLD_DATE'] = pd.to_datetime(clean_dates, unit='D', origin='julian')
print (df['MXPLD_DATE'])
0         2015-06-10 12:00:00
1         2015-05-12 12:00:00
2         2015-05-12 12:00:00
3         2015-05-12 12:00:00
4         2015-05-12 12:00:00
5         2015-05-12 12:00:00
6         2015-05-12 12:00:00
7         2015-05-12 12:00:00
8         2015-05-12 12:00:00
9         2015-05-12 12:00:00
10        2015-05-12 12:00:00