Converting date and time combination from dataframe to timestamp python-CodePudding

All,

I have a pandas dataframe with a column (live.updated) containing a date and time combination as follows:


    live               live.updated  live.latitude  live.longitude  live.altitude  live.direction  live.speed_horizontal  live.speed_vertical live.is_ground


1   NaN  2022-10-11T17:45:54 00:00         -27.55          143.20        11277.6           139.0                853.772                  0.0          False

2   NaN  2022-10-11T17:46:19 00:00         -45.35          169.88         5791.2            44.0                518.560                  0.0          False

I would like to convert each element in the live.updated column into a timestamp (in seconds). I have tried applying the datetime module as described in https://stackabuse.com/converting-strings-to-datetime-in-python/, but this method does not seem fit with my specific case. Anyone know how to solve this such that I get a timestamp t = 12419933 seconds (for example)

CodePudding user response：

here is one way to do it

(((pd.to_datetime(df['live.updated']).dt.hour * 60)   pd.to_datetime(df['live.updated']).dt.minute ) * 60
   pd.to_datetime(df['live.updated']).dt.second)

# extract time from the date, after converting ito datetime
tm=pd.to_datetime(df['live.updated']).dt.time

# get total_seconds using timedelta
pd.to_timedelta(tm.astype(str)).dt.total_seconds()

1    63954
2    63979
Name: live.updated, dtype: int64

CodePudding user response：

If I understand you right, you want to get unix timestamps out of the datetimes:

df["live.updated"] = pd.to_datetime(df["live.updated"]).astype(int) // 10 ** 9
print(df)

Prints:

   live  live.updated  live.latitude  live.longitude  live.altitude  live.direction  live.speed_horizontal  live.speed_vertical  live.is_ground
1   NaN    1665510354         -27.55          143.20        11277.6           139.0                853.772                  0.0           False
2   NaN    1665510379         -45.35          169.88         5791.2            44.0                518.560                  0.0           False