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Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
I have a set of objects and their positions over time. I would like to get the distance between each car and their farmost neighbour, and calculate an average of this for each time point. An example dataframe is as follows:
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Python Code:
import pandas as pd
time = [0, 0, 0, 1, 1, 2, 2]
x = [216, 218, 217, 280, 290, 130, 132]
y = [13, 12, 12, 110, 109, 3, 56]
car = [1, 2, 3, 1, 3, 4, 5]
df = pd.DataFrame({'time': time, 'x': x, 'y': y, 'car': car})
import numpy as np
def g(df):
    time = df.time.tolist()
    car = df.car.tolist()
    farmost_neighbour = []
    euclidean_distance = []
    for i in range(len(df)):
        n = 0
        d = 0
        for j in range(len(df)):
            if df.loc[i, 'time'] == df.loc[j, 'time'] and df.loc[i, 'car'] != df.loc[j, 'car']:
                t = np.sqrt(((df.loc[i, 'x'] - df.loc[j, 'x'])**2) + ((df.loc[i, 'y'] - df.loc[j, 'y'])**2))
                if t >= d:
                    d = t
                    n = df.loc[j, 'car']
        farmost_neighbour.append(n)
        euclidean_distance.append(d)
    return pd.DataFrame({'time': time, 'car': car, 'farmost_neighbour': farmost_neighbour, 'euclidean_distance': euclidean_distance})
df = g(df.copy())
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