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遍历python元组列表并应用函数

[英]Looping over a python list of tuples and applying a function

I have a data frame in this format: 我有这种格式的数据框:

  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: 每行都是轨迹数据,我必须使用func_dist函数查找轨迹之间的距离,如下所示:

 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.. 我有兴趣轨迹的索引列表l ..

l = [0,1,3]

I must find the distance between all the possible pairs of trajectories; 我必须找到所有可能的轨迹对之间的距离; in the case above, this is 0-1, 0-3, and 1-3. 在上述情况下,它是0-1、0-3和1-3。 I know how to generate a list of pairs using 我知道如何使用

 pairsets = list(itertools.combinations(l, 2)) 

which returns 哪个返回

 [(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. 由于该列表可能有100多个索引,因此我试图使此过程自动化,并将每对之间计算出的距离存储在new_df数据帧中。

I tried the following code for distance computation: 我尝试了以下代码进行距离计算:

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. 但是它只计算最后一对1-3。 How to calculate all of the pairs and create a new data frame like this: 如何计算所有对,并创建一个新的数据框,如下所示:

  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. 这仅仅是正确处理索引的问题。 For each pair, you grab the two indices, assign your data sets, and compute the distance. 对于每一对,您都抓住两个索引,分配数据集,然后计算距离。

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: 您可以合并前两行:

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. dist_table提供了一个二维列表,您应该可以通过一个简单的PANDAS调用将其转换为新的数据框。

Does that get you moving? 那会让你动起来吗?

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