CODEWITHSUNDEEP
pythonpython-3.xpandas
smokinjoe
smokinjoe

Reputation: 93

How to add integer (seconds) to hh:mm:ss format in Python?

I have the following data frame in python where am trying to compute column 'New time' by adding 'Duration' (which is in seconds) to the 'start_time'

Serial  start_date     start_time     Duration(seconds)  New time
    A   5/22/2017       10:37:24        216 
    A   5/22/2017       10:37:26        213 
    A   5/22/2017       10:37:29         3  
    A   5/22/2017       10:39:55         60 
    A   5/22/2017       10:51:50        380 
    A   5/22/2017       10:51:57        339

I want to add duration into start_time. The duration is in seconds. 'New time' is expected in hh:mm:ss format.

I tried looking for similar queries in the forum but could not get around this.

Below is the info 

data.info()

start_date         13661 non-null object
start_time         13661 non-null object
Duration           13661 non-null int64

I tried taking a cue from a similar problem in the forum, using datetime

data.newtime = data.start_time + datetime.timedelta(data.Duration)

when i execute this am getting the following error : TypeError: unsupported type for timedelta days component: Series

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-95-fdfac1490ba5> in <module>()
----> 1 data.newtime = data.start_time + datetime.timedelta(data.Duration)

TypeError: unsupported type for timedelta days component: Series

Not sure how to go about it. New to python.

Help appreciated TIA

Upvotes: 1

Views: 2618

Answers (2)

jezrael
jezrael

Reputation: 862681

You can use to_timedelta and output is timedelta too:

df['New time'] = pd.to_timedelta(df['start_time']) + 
                 pd.to_timedelta(df['Duration(seconds)'], unit='s')
print (df)
  Serial start_date start_time  Duration(seconds) New time
0      A  5/22/2017   10:37:24                216 10:41:00
1      A  5/22/2017   10:37:26                213 10:40:59
2      A  5/22/2017   10:37:29                  3 10:37:32
3      A  5/22/2017   10:39:55                 60 10:40:55
4      A  5/22/2017   10:51:50                380 10:58:10
5      A  5/22/2017   10:51:57                339 10:57:36

But if seconds is more, output is changed, because there are also days:

print (df)
  Serial start_date start_time  Duration(seconds)
0      A  5/22/2017   10:37:24                216
1      A  5/22/2017   10:37:26             213000
2      A  5/22/2017   10:37:29                  3
3      A  5/22/2017   10:39:55                 60
4      A  5/22/2017   10:51:50                380
5      A  5/22/2017   10:51:57                339

df['New time'] = pd.to_timedelta(df['start_time']) + 
                 pd.to_timedelta(df['Duration(seconds)'], unit='s')
print (df)
  Serial start_date start_time  Duration(seconds)        New time
0      A  5/22/2017   10:37:24                216 0 days 10:41:00
1      A  5/22/2017   10:37:26             213000 2 days 21:47:26
2      A  5/22/2017   10:37:29                  3 0 days 10:37:32
3      A  5/22/2017   10:39:55                 60 0 days 10:40:55
4      A  5/22/2017   10:51:50                380 0 days 10:58:10
5      A  5/22/2017   10:51:57                339 0 days 10:57:36

Also is posible add datetime:

df['New date'] = pd.to_datetime(df['start_date']) + \
                 pd.to_timedelta(df['start_time']) +  \
                 pd.to_timedelta(df['Duration(seconds)'], unit='s')
print (df)
  Serial start_date start_time  Duration(seconds)            New date
0      A  5/22/2017   10:37:24                216 2017-05-22 10:41:00
1      A  5/22/2017   10:37:26                213 2017-05-22 10:40:59
2      A  5/22/2017   10:37:29                  3 2017-05-22 10:37:32
3      A  5/22/2017   10:39:55                 60 2017-05-22 10:40:55
4      A  5/22/2017   10:51:50                380 2017-05-22 10:58:10
5      A  5/22/2017   10:51:57                339 2017-05-22 10:57:36

df['New date'] = pd.to_datetime(df['start_date']) + \
                 pd.to_timedelta(df['start_time']) +  \
                 pd.to_timedelta(df['Duration(seconds)'], unit='s')
print (df)
  Serial start_date start_time  Duration(seconds)            New date
0      A  5/22/2017   10:37:24                216 2017-05-22 10:41:00
1      A  5/22/2017   10:37:26             213000 2017-05-24 21:47:26
2      A  5/22/2017   10:37:29                  3 2017-05-22 10:37:32
3      A  5/22/2017   10:39:55                 60 2017-05-22 10:40:55
4      A  5/22/2017   10:51:50                380 2017-05-22 10:58:10
5      A  5/22/2017   10:51:57                339 2017-05-22 10:57:36

---

And if need convert timedelta to string in format HH:MM:SS and lost days (if exist):

df['New time'] = pd.to_timedelta(df['start_time']) + 
                 pd.to_timedelta(df['Duration(seconds)'], unit='s')
df['New time'] = pd.to_datetime(df['New time']).dt.strftime('%H:%M:%S')
print (df)
  Serial start_date start_time  Duration(seconds)  New time
0      A  5/22/2017   10:37:24                216  10:41:00
1      A  5/22/2017   10:37:26             213000  21:47:26
2      A  5/22/2017   10:37:29                  3  10:37:32
3      A  5/22/2017   10:39:55                 60  10:40:55
4      A  5/22/2017   10:51:50                380  10:58:10
5      A  5/22/2017   10:51:57                339  10:57:36

Upvotes: 3

Maciek
Maciek

Reputation: 3234

Here is a snippet that may help you solve the problem yourself:

from datetime import datetime, timedelta

my_date = datetime.strptime('5/22/2017 10:37:24', '%m/%d/%Y %H:%M:%S')
my_time_diff = timedelta(seconds=216)
my_new_date = my_date + my_time_diff
print(my_new_date.strftime('%m/%d/%Y %H:%M:%S'))

Useful resource:

Upvotes: 0

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