python - From TimeDelta to float days in Pandas

Question

I have a TimeDelta column with values that look like this:

2 days 21:54:00.000000000

I would like to have a float representing the number of days, let's say here 2+21/24 = 2.875, neglecting the minutes. Is there a simple way to do this ? I saw an answer suggesting

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

But I get "AttributeError: 'str' object has no attribute 'item' "

Numpy version is '1.10.4' Pandas version is u'0.17.1'

The columns has originally been obtained with:

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')

in a first script. Then in a second one:

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']

Maybe saving it to csv change their types back to string? The transformation I'm trying to do is in a third script.

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

Answer 1

You can use pd.to_timedelta or np.timedelta64 to define a duration and divide by this:

# set up as per @EdChum
df['total_days_td'] = df['time_delta'] / pd.to_timedelta(1, unit='D')
df['total_days_td'] = df['time_delta'] / np.timedelta64(1, 'D')