finding latest trip information from a large data frame - python-3.x

Question

I have one requirement:
I have a dataframe "df_input" having 20M rows which includes trip details. columns are "vehicle-no", "geolocation","start","end".
For each of the vehicle number there are multiple rows each having different geolocation for different trips.
Now I want to create a new dataframe df_final which will have only the first record for all of the vehicle-no. How can do that in efficient way?
I used something like below which is taking more than 5 hours to complete:
import dfply as dp
from dfply import X
output_df_columns = ["vehicle-no","start", "end", "geolocations"]
df_final = pd.DataFrame(columns = output_df_columns) #create empty dataframe
unique_vehicle_no = list(df_input["vehicle-no"].unique())
df_input.sort_values(["start"],inplace=True)
for each_vehicle in unique_vehicle_no:
df_temp = (df_input >> dp.mask(X.vehicle-no == each_vehicle))
df_final = df_final.append(df_temp.head(1),ignore_index=True, sort=False)

Answer 1

I think this will work out
import pandas as pd
import numpy as np
df_input=pd.DataFrame(np.random.randint(10,size=(1000,3)),columns=['Geolocation','start','end'])
df_input['vehicle_number']=np.random.randint(100,size=(1000))
print(df_input.shape)
print(df_input['vehicle_number'].nunique())
df_final=df_input.groupby('vehicle_number').apply(lambda x : x.head(1)).reset_index(drop=True)
print(df_final['vehicle_number'].nunique())
print(df_final.shape)