I have a dataframe as shown below. Which is inspector tracking data (latitude and longitude) almost in each minute.
Inspector_ID Timestamp Latitude Longitude
1 2018-07-24 7:31:00 100.491491 13.725239
1 2018-07-24 7:31:01 101.491491 15.725239
1 2018-07-24 7:32:04 104.491491 14.725239
1 2018-07-24 7:33:06 102.491491 10.725239
2 2018-07-24 8:35:08 105.491491 8.725239
2 2018-07-24 8:36:10 101.491491 15.725239
2 2018-07-24 8:37:09 101.491491 12.725239
2 2018-07-24 8:39:00 106.491491 16.725239
From the above data I would like find out the distance travelled by an inspector from each consecutive tracking data (latitude and longitude of the inspector).
Expected output (example)
Inspector_ID Timestamp Latitude Longitude Distance
1 2018-07-24 7:31:00 100.491491 13.725239 nan
1 2018-07-24 7:31:01 101.491491 15.725239 2.3
1 2018-07-24 7:32:04 104.491491 14.725239 1.2
1 2018-07-24 7:33:06 102.491491 10.725239 3.6
2 2018-07-24 8:35:08 105.491491 8.725239 nan
2 2018-07-24 8:36:10 101.491491 15.725239 5.6
2 2018-07-24 8:37:09 101.491491 12.725239 2.1
2 2018-07-24 8:39:00 106.491491 16.725239 3
Here I would like to calculate the distance groupby Inspector_ID.
Note: The number populated in Distance column are not the correct distance.
I am not aware, how to calculate distance using latitude and longitude. I am very new in pandas as well.
Use GroupBy.diff:
df['distance']=df.groupby('Inspector_ID')[['Latitude','Longitude']].diff().pow(2).sum(axis=1,min_count=1).pow(1/2)
print(df)
Inspector_ID Timestamp Latitude Longitude distance
0 1 2018-07-24 07:31:00 100.491491 13.725239 NaN
1 1 2018-07-24 07:31:01 101.491491 15.725239 2.236068
2 1 2018-07-24 07:32:04 104.491491 14.725239 3.162278
3 1 2018-07-24 07:33:06 102.491491 10.725239 4.472136
4 2 2018-07-24 08:35:08 105.491491 8.725239 NaN
5 2 2018-07-24 08:36:10 101.491491 15.725239 8.062258
6 2 2018-07-24 08:37:09 101.491491 12.725239 3.000000
7 2 2018-07-24 08:39:00 106.491491 16.725239 6.403124
if you want 0 instead of NaN remove min_count = 1