I am working on some time series data and the timestamp only includes the time( HH:MM:SS
) , but I need to add the YY/MM/DD
to the timestamp. I working with pandas dataframe.
I tried using pd.to_datetime(), but it enters the current date that I call it.
df_17c = pd.read_csv(file_17c,sep ='\t', header = None,names=['TimeStamp','x','y','z'], usecols =[0,3,4,5])
df_17s = pd.read_csv(file_17s,sep ='\t', header = None,names = ['TimeStamp','x','y','z'],usecols =[0,1,2,3])
TimeStamp x y z
0 23:59:58 26799 -218 0
1 23:59:58 26797 -218 0
2 23:59:58 26795 -218 0
3 23:59:58 26793 -218 0
4 23:59:58 26792 -217 0
The "TimeStamp" column is a object type ( string). When I convert using .to_datetime()
it yields datetime object with the current date.
df_17c["Date"]= pd.to_datetime(df_17c['TimeStamp'])
TimeStamp x y z
0 2019-06-26 23:59:58 26799 -218 0
1 2019-06-26 23:59:58 26797 -218 0
2 2019-06-26 23:59:58 26795 -218 0
3 2019-06-26 23:59:58 26793 -218 0
4 2019-06-26 23:59:58 26792 -217 0
This isn't probably the most efficient way, but it's simple (basically add the date to the front of the string)
date = '2017-01-09T' # or whatever (note the T)
pd.to_datetime(df['TimeStamp'].apply(lambda s: date+s))
example
df = pd.DataFrame({'time': ['08:11:09', '17:09:34']})
# time
# 0 08:11:09
# 1 17:09:34
date_func = '2017-01-09T{}'.format # avoid the use of lambda + more efficient
df['datetime'] = pd.to_datetime(df['time'].apply(date_func))
output
time datetime
0 08:11:09 2017-01-09 08:11:09
1 17:09:34 2017-01-09 17:09:34
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