Python 函数或嵌套循环，用于对列中的每个唯一项进行距离计算

Question

基本上假设我有 3 辆车和一堆 x/y 坐标，如下所示：

车号 ___ x 库尔 ___ y 库尔

1 _________ 54 _____ 251 ___________ 57 _____ 261 _________ 54 _____ 292 _____ 52 _____ 242 ___________ 56 _____ 282 ___________ 57 _____ 293 _____ 51 _____ 253 _____ 54 _____263 _______ 59 _____ 29

我需要我的代码做的是计算每辆车从坐标开始的位移或行驶距离，输出显示类似

汽车 __ 排量
1 ________ 92 ________ 5
3 ________ 7

我目前拥有的在下面，绝对不起作用

displacement  = 0
for (car number, x coor, y coor) in coorset:
for i in car number:
displacement(i) = displacement  + (df[coor x] **2 + df[coor y] **2)**.5
print (displacement)
print(car number)

我是python的新手，所以请原谅我的错误，我真的很困惑。

Answer 1

from pandas import DataFrame
# create data
data = DataFrame([
(1, 54, 25),
(1, 57, 26),
(1, 54, 29),
(2, 52, 24),
(2, 56, 28),
(2, 57, 29),
(3, 51, 25),
(3, 54, 26),
(3, 59, 29),
], columns=['car_number', 'x_coord', 'y_coord'])

# calculate distances
data['distance'] = (
(data['x_coord'] - data['x_coord'].shift()) ** 2 +
(data['y_coord'] - data['y_coord'].shift()) ** 2
) ** 0.5
# ignore distances between points for different cars
data['same_car'] = data['car_number'] == data['car_number'].shift()
data['distance'] = data['distance'] * data['same_car']
# group distances by car and sum
distances = data.groupby('car_number')['distance'].sum().reset_index()

Answer 2

这应该有效。我从与当前车号对应的数据帧中获取了一部分，对其进行修改以包含位移，然后将其替换到原始数据帧中。

data["displacement"] = 0
def distance_x(df, i):
return (df.iloc[i, 1] - df.iloc[i + 1, 1]) ** 2
def distance_y(df, i):
return (df.iloc[i, 2] - df.iloc[i + 1, 2]) ** 2
def total_displacement(df):
cars = df["car_number"].unique()
for car_num in cars:
df_sel = df[df["car_number"] == car_num].copy()
for i in range(len(df_sel) - 1):
distance = (distance_x(df_sel, i) + distance_y(df_sel, i)) ** (1/2)
df_sel.iloc[i + 1, 3] = distance + df_sel.iloc[i, 3]
df[df["car_number"] == df_sel.iloc[0,0]] = df_sel    
return df

total_displacement(data)
print(data)
car_number  x_coord  y_coord  displacement
0         1.0     54.0     25.0      0.000000
1         1.0     57.0     26.0      3.162278
2         1.0     54.0     29.0      7.404918
3         2.0     52.0     24.0      0.000000
4         2.0     56.0     28.0      5.656854
5         2.0     57.0     29.0      7.071068
6         3.0     51.0     25.0      0.000000
7         3.0     54.0     26.0      3.162278
8         3.0     59.0     29.0      8.993230