CODEWITHSUNDEEP
python
M A
M A

Reputation: 45

Iterating through a column of pandas dataframe that has time values and return seconds

I have a column in a pandas dataframe that looks like this:

Time:
16:45:15
16:45:31
16:45:59
.
.

I want to convert these times values into seconds and I did the following:

def get_sec(arr):
    h,m,s = arr.split(':')
    return int(h) * 3600 + int(m) * 60 + int(s)

sec_val = []
for i in range(len(df['Time'])):
    sec_val.append(get_sec(df['Time'][i]))

and it gives this error:

'datatime.time' object has no attribute 'split'

Although if I used the same code for one string it works, but when I use it in a for loop it gives this error, any idea on how to solve it?

Thanks

Upvotes: 0

Views: 55

Answers (1)

Kunal Sawant
Kunal Sawant

Reputation: 493

In [24]: df['TimeInSeconds'] = df['Time'].apply(lambda x : x.hour *3600 + x.minute * 60 + x.second )

Looks like your column is type datetime.time so you can directly use it's attributes like hour , minute , second rather than doing string split as above. hope it helps :)

Edit:

In case of string we can convert all in time objects then use the apply

import datetime

In [38]: df['Time' ] = df['Time'].astype(str).apply(lambda x : datetime.datetime.strptime(x, '%H:%M:%S' ).time() )

In [39]: df['TimeInSeconds'] = df['Time'].apply(lambda x : x.hour *3600 + x.minute * 60 + x.second )

Upvotes: 2

Related Questions