I have a data frame in this format:
vid points
0 1 [[0,1], [0,2, [0,3]]
1 2 [[1,2], [1,4], [1,9], [1,7]]
2 3 [[2,1], [2,3], [2,8]]
3 4 [[3,2], [3,4], [3,5],[3,6]]
Each row is trajectory data, and I have to find distance between the trajectories with a function func_dist
, like this:
x = df.iloc[0]["points"].tolist()
y = df.iloc[3]["points"].tolist()
func_dist(x, y)
I have a list l
of indices for trajectories of interest..
l = [0,1,3]
I must find the distance between all the possible pairs of trajectories; in the case above, this is 0-1, 0-3, and 1-3. I know how to generate a list of pairs using
pairsets = list(itertools.combinations(l, 2))
which returns
[(0,1), (0,3), (1,3)]
Since the list may have over 100 indices, I am trying to automate this process and store the distances calculated between each pair in a new_df
data frame.
I tried the following code for distance computation:
for pair in pairsets:
a, b = [m[0] for m in pairssets], [n[1] for n in pairsets]
for i in a:
x = df.iloc[i]["points"].tolist()
for j in b:
y = df.iloc[j]["points"].tolist()
dist = func_dist(x, y)
But it calculates only the last pair, 1-3. How to calculate all of the pairs and create a new data frame like this:
traj1 traj2 distance
0 1 some_val
0 3 some_val
1 3 some_val
This is simply a matter of handling your indices properly. For each pair, you grab the two indices, assign your data sets, and compute the distance.
dist_table = []
for pair in pairsets:
i, j = pair
x = df.iloc[i]["points"].tolist()
y = df.iloc[j]["points"].tolist()
dist = func_dist(x, y)
dist_table.append( [i, j, dist] )
You can combine the first two lines:
for i, j in pairsets:
The dist_table
gives you a 2D list that you should be able to convert to a new data frame with a simple PANDAS call.
Does that get you moving?
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