从TimeDelta到Pandas的漂浮日

2天21：54：00.000000000

我想有一个代表天数的浮点数,让我们说这里2 21/24 = 2.875,忽略了分钟.
有一个简单的方法吗？
我看到了一个答案

res['Ecart_lacher_collecte'].apply(lambda x: float(x.item().days+x.item().hours/24.))

但我得到“AttributeError：’str’对象没有属性’item’”

Numpy版本是’1.10.4′
熊猫版本是u’0.17.1′

这些列最初是通过以下方式获得的：

lac['DateHeureLacher'] = pd.to_datetime(lac['Date lacher']+' '+lac['Heure lacher'],format='%d/%m/%Y %H:%M:%s')
cap['DateCollecte'] = pd.to_datetime(cap['Date de collecte']+' '+cap['Heure de collecte'],format='%d/%m/%Y %H:%M:%s')

在第一个脚本中.然后在第二个：

res = pd.merge(lac, cap, how='inner', on=['Loc'])
res['DateHeureLacher']  = pd.to_datetime(res['DateHeureLacher'],format='%Y-%m-%d %H:%M:%s')
res['DateCollecte']  = pd.to_datetime(res['DateCollecte'],format='%Y-%m-%d %H:%M:%s')
res['Ecart_lacher_collecte'] = res['DateCollecte'] - res['DateHeureLacher']

也许将它保存到csv将其类型更改回字符串？我想要做的转变是在第三个脚本中.

Sexe_x  PiegeLacher latL    longL   Loc Col_x   DateHeureLacher Nb envolees PiegeCapture    latC    longC   Col_y   Sexe_y  Effectif    DateCollecte    DatePose    Ecart_lacher_collecte   dist_m
M   Q0-002  1629238 237877  H   Rouge   2011-02-04 17:15:00 928 Q0-002  1629238 237877  Rouge   M   1   2011-02-07 15:09:00 2011-02-07 12:14:00 2 days 21:54:00.000000000   0
M   Q0-002  1629238 237877  H   Rouge   2011-02-04 17:15:00 928 Q0-002  1629238 237877  Rouge   M   4   2011-02-07 12:14:00 2011-02-07 09:42:00 2 days 18:59:00.000000000   0
M   Q0-002  1629238 237877  H   Rouge   2011-02-04 17:15:00 928 Q0-003  1629244 237950  Rouge   M   1   2011-02-07 15:10:00 2011-02-07 12:16:00 2 days 21:55:00.000000000   75

res.info()：

Sexe_x                   922 non-null object
PiegeLacher              922 non-null object
latL                     922 non-null int64
longL                    922 non-null int64
Loc                      922 non-null object
Col_x                    922 non-null object
DateHeureLacher          922 non-null object
Nb envolees              922 non-null int64
PiegeCapture             922 non-null object
latC                     922 non-null int64
longC                    922 non-null int64
Col_y                    922 non-null object
Sexe_y                   922 non-null object
Effectif                 922 non-null int64
DateCollecte             922 non-null object
DatePose                 922 non-null object
Ecart_lacher_collecte    922 non-null object
dist_m                   922 non-null int64

解决方法:

您可以使用dt.total_seconds并将其除以一天中的总秒数,例如：

In [25]:
df = pd.DataFrame({'dates':pd.date_range(dt.datetime(2016,1,1, 12,15,3), periods=10)})
df

Out[25]:
                dates
0 2016-01-01 12:15:03
1 2016-01-02 12:15:03
2 2016-01-03 12:15:03
3 2016-01-04 12:15:03
4 2016-01-05 12:15:03
5 2016-01-06 12:15:03
6 2016-01-07 12:15:03
7 2016-01-08 12:15:03
8 2016-01-09 12:15:03
9 2016-01-10 12:15:03

In [26]:
df['time_delta'] = df['dates'] - pd.datetime(2015,11,6,8,10)
df

Out[26]:
                dates       time_delta
0 2016-01-01 12:15:03 56 days 04:05:03
1 2016-01-02 12:15:03 57 days 04:05:03
2 2016-01-03 12:15:03 58 days 04:05:03
3 2016-01-04 12:15:03 59 days 04:05:03
4 2016-01-05 12:15:03 60 days 04:05:03
5 2016-01-06 12:15:03 61 days 04:05:03
6 2016-01-07 12:15:03 62 days 04:05:03
7 2016-01-08 12:15:03 63 days 04:05:03
8 2016-01-09 12:15:03 64 days 04:05:03
9 2016-01-10 12:15:03 65 days 04:05:03

In [27]:
df['total_days_td'] = df['time_delta'].dt.total_seconds() / (24 * 60 * 60)
df

Out[27]:
                dates       time_delta  total_days_td
0 2016-01-01 12:15:03 56 days 04:05:03      56.170174
1 2016-01-02 12:15:03 57 days 04:05:03      57.170174
2 2016-01-03 12:15:03 58 days 04:05:03      58.170174
3 2016-01-04 12:15:03 59 days 04:05:03      59.170174
4 2016-01-05 12:15:03 60 days 04:05:03      60.170174
5 2016-01-06 12:15:03 61 days 04:05:03      61.170174
6 2016-01-07 12:15:03 62 days 04:05:03      62.170174
7 2016-01-08 12:15:03 63 days 04:05:03      63.170174
8 2016-01-09 12:15:03 64 days 04:05:03      64.170174
9 2016-01-10 12:15:03 65 days 04:05:03      65.170174

从TimeDelta到Pandas的漂浮日

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