计算每个唯一 ID 行驶的距离总和

Question

I have a data-frame which has three columns. One column contains x-coordinates, another column with y-coordinates. also, as you can see, there is a 'trackid' column -- this column associates all of the x and y coordinates with a specific, unique trackid.

    trackiD   X_COORDINATES     Y_COORDINATES
        
     2        542.299805        23.388090
     2        544.108215        23.575758
     2        545.300598        23.962421
     2        546.417053        25.049328
     2        546.198669        24.830357
     2        546.724915        24.916084
     2        547.037048        24.918982
     2        547.011963        24.785202
     2        547.649231        24.845772
     3        547.600525        24.613401
     3        547.891479        24.268734
     3        548.580505        24.459103
     3        548.144409        23.915531
     3        548.626770        23.922005
     4        548.527222        24.134670
     4        548.504211        23.642254
     4        548.936584        24.028818
     4        548.627869        23.295454

What I am trying to do is the following:

• take each pair of x and y coordinates and calculate the increments of distance traveled between them using the pythagorean distance formula: (sqrt(x2-x1)^2 + (y2-y1)^2), adding each distance increment to a list, then taking the sum of all increments in the list to get the total distance traveled -- also important to note, I am doing this calculation only for each set of coordinates within a unique trackid. ie. calculate sum of the distance increments for trackid 2, then do the same process separately for trackid 3 and 4 and so forth -- ultimately storing all the total distances traveled per each unique track ID in a new list.

Here is my current code -- it runs, but the issue is , it outputs a list with just one single, large, likely incorrect value (displayed below). also the 'value' variable seems to have been cut off and displayed across multiple lines here on stackoverflow but this is not the case when I run it in jupyter notebook.

       def pythag_dis(U_id):
          c = data.Unique_id == U_id
          df = data[c]
          df.reset_index(inplace = True)
          k = sorted(df.trackId.unique())
          i = 0
          j = 1
          length = len(k)
          while i < length: 
            condition = df.trackId == k[i]
            df2 = df[condition]
            df2.reset_index(inplace = True)
            value = 
           math.sqrt((df.Object_Center_0.iloc[j] - 
           df.Object_Center_0.iloc[i])**2 + 
           (df.Object_Center_1.iloc[j] - 
           df.Object_Center_1.iloc[i])**2)
           mylist = []
           mylist.append(value)
           fulldistance = sum(mylist)
           mylist2 = []
           mylist2.append(fulldistance)
           i+=1
      return mylist2
    pythag_dis('1CCM0701')

OUTPUT: [1976.075585650214]

Answer 1

First create two new columns, X_SHIFTED and Y_SHIFTED that represents the next point's coordinates for each track ID. We do this by combining df.groupby and df.shift :

df[['X_SHIFTED', 'Y_SHIFTED']] = df.groupby('trackiD').shift()

Then, simply use the euclidean distance formula between points ( X_COORDINATES , Y_COORDINATES ) and ( X_SHIFTED , Y_SHIFTED ). We can do this using df.apply row-wise ( axis=1 ), along with math.dist :

import math

df['DIST'] = df.apply(
    lambda row: math.dist(
        (row['X_COORDINATES'], row['Y_COORDINATES']),
        (row['X_SHIFTED'], row['Y_SHIFTED'])
    ), axis=1)

output:

    trackiD  X_COORDINATES  Y_COORDINATES   X_SHIFTED  Y_SHIFTED      DIST
0         2     542.299805      23.388090         NaN        NaN       NaN
1         2     544.108215      23.575758  542.299805  23.388090  1.818122
2         2     545.300598      23.962421  544.108215  23.575758  1.253509
3         2     546.417053      25.049328  545.300598  23.962421  1.558152
4         2     546.198669      24.830357  546.417053  25.049328  0.309257
5         2     546.724915      24.916084  546.198669  24.830357  0.533183
6         2     547.037048      24.918982  546.724915  24.916084  0.312146
7         2     547.011963      24.785202  547.037048  24.918982  0.136112
8         2     547.649231      24.845772  547.011963  24.785202  0.640140
9         3     547.600525      24.613401         NaN        NaN       NaN
10        3     547.891479      24.268734  547.600525  24.613401  0.451054
11        3     548.580505      24.459103  547.891479  24.268734  0.714841
12        3     548.144409      23.915531  548.580505  24.459103  0.696886
13        3     548.626770      23.922005  548.144409  23.915531  0.482404
14        4     548.527222      24.134670         NaN        NaN       NaN
15        4     548.504211      23.642254  548.527222  24.134670  0.492953
16        4     548.936584      24.028818  548.504211  23.642254  0.579981
17        4     548.627869      23.295454  548.936584  24.028818  0.795693

To get each track's sum of distances, you can then use:

df.groupby('trackiD')['DIST'].sum()

output:

trackiD
2    6.560621
3    2.345185
4    1.868628
Name: DIST, dtype: float64

Answer 2

A possible solution using Pandas: I use pandas groupby shift to match the coordinates, calculate the distance and then sum the distance in the groups:

import math
import numpy as np
import pandas as pd

def distance(row):
    x1, y1, x2, y2 = row["X_COORDINATES"], row["Y_COORDINATES"], row["X2"], row["Y2"]
    if np.isnan(x2) or np.isnan(y2):
        return 0
    return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)

df["X2"] = df.groupby("trackiD")["X_COORDINATES"].shift(-1)
df["Y2"] = df.groupby("trackiD")["Y_COORDINATES"].shift(-1)

df["distance"] = df.apply(distance, axis=1)
df.groupby("trackiD")["distance"].sum()

Output:

trackiD
2    6.560621
3    2.345185
4    1.868628
Name: distance, dtype: float64

Test dataframe:

df = pd.DataFrame(
    {
        "trackiD": {
            0: 2,
            1: 2,
            2: 2,
            3: 2,
            4: 2,
            5: 2,
            6: 2,
            7: 2,
            8: 2,
            9: 3,
            10: 3,
            11: 3,
            12: 3,
            13: 3,
            14: 4,
            15: 4,
            16: 4,
            17: 4,
        },
        "X_COORDINATES": {
            0: 542.299805,
            1: 544.108215,
            2: 545.300598,
            3: 546.417053,
            4: 546.198669,
            5: 546.724915,
            6: 547.037048,
            7: 547.011963,
            8: 547.649231,
            9: 547.600525,
            10: 547.891479,
            11: 548.580505,
            12: 548.144409,
            13: 548.62677,
            14: 548.527222,
            15: 548.504211,
            16: 548.936584,
            17: 548.627869,
        },
        "Y_COORDINATES": {
            0: 23.38809,
            1: 23.575758,
            2: 23.962421,
            3: 25.049328,
            4: 24.830357,
            5: 24.916084,
            6: 24.918982,
            7: 24.785202,
            8: 24.845772,
            9: 24.613401,
            10: 24.268734,
            11: 24.459103,
            12: 23.915531,
            13: 23.922005,
            14: 24.13467,
            15: 23.642254,
            16: 24.028818,
            17: 23.295454,
        },
    }
)