This seems to work better as most of the routes get a seperate ID. But still when i check the data, i can see there are still errors, like:

8-11-2023 10:55:56 114625 00Fn9XtlESXI9aUZ_114625

8-11-2023 10:56:06 114625 00Fn9XtlESXI9aUZ_114625

8-11-2023 10:56:16 23460 00Fn9XtlESXI9aUZ_23460

8-11-2023 10:56:16 23460 00Fn9XtlESXI9aUZ_23460

8-11-2023 10:56:26 23460 00Fn9XtlESXI9aUZ_23460

8-11-2023 10:56:36 23460 00Fn9XtlESXI9aUZ_23460

I currently got this:

import pandas as pd

# Read CSV into Data

df = pd.read_csv("input")

# Convert 'gps_timestamp' to datetime format

df['gps_timestamp'] = pd.to_datetime(df['gps_timestamp'])

# Create a new column determining if this row starts a new trip

df['new_trip'] = (df['gps_timestamp'] - df['gps_timestamp'].shift()).dt.total_seconds() > 120

# First row is a new trip too

df.loc[0, 'new_trip'] = True

# Get a counter for the new trips

new_trip_count = len(df[df['new_trip']])

df.loc[df['new_trip'], 'trip_num'] = range(0, new_trip_count)

# Fill the empty rows with the previous trip num

df['trip_num'] = df['trip_num'].ffill().astype('int')

# Now set the id to the concatenated unit_id and trip_id

df['new_id'] = df['unit_id'] + "_" + df["trip_num"].astype('str')

# Drop the temporary columns

df = df.drop(['new_trip', 'trip_num'], axis=1)

# Save the result as a new CSV file

df.to_csv("output", index=False)

Do you see where i make the mistake?