Question

我想计算自变量event等于1以来的覆盖距离。重要的是应该为每个ID计算距离。

我的数据集由date，car_id，latitude，longitude以及指示事件与否的伪变量组成。我用来计算距离的公式是：

def Distance(Latitude, Longitude, LatitudeDecimal, LongitudeDecimal):
  az12,az21,dist = wgs84_geod.inv(Longitude, Latitude, LongitudeDecimal, LatitudeDecimal)
  return dist

我想要的是计算每个event==1到最后一个car_id以来两个地理位置之间的距离，因此计算列distance_since_event：

date    car_id  latitude    longitude   event   distance_since_event
01/01/2019  1   43.5863 7.12993 0   -1
01/01/2019  2   44.3929 8.93832 0   -1
02/01/2019  1   43.5393 7.03134 1   -1
02/01/2019  2   39.459462   -0.312280   0   -1
03/01/2019  1   44.3173 84.942  0   calculation=(distance from 02/01/2019-03/01/2019 for ID=1)
03/01/2019  2   -12.3284    -9.04522    1   -1
04/01/2019  1   -36.8414    17.4762 0   calculation=(distance from 02/01/2019-04/01/2019 for ID=1)
04/01/2019  2   43.542  10.2958 0   calculation=(distance from 03/01/2019-04/01/2019 for ID=2)
05/01/2019  1   43.5242 69.473  0   calculation=(distance from 02/01/2019-05/01/2019 for ID=1)
05/01/2019  2   37.9382 23.668  1   calculation=(distance from 03/01/2019-05/01/2019 for ID=2)
06/01/2019  1   4.4409  89.218  1   calculation=(distance from 02/01/2019-06/01/2019 for ID=1)
06/02/2019  2   25.078037   -77.328900  0   calculation=(distance from 05/01/2019-06/01/2019 for ID=2)

Answer 1

在这里为您提供帮助的关键功能是pandas.merge_asof和allow_exact_matches=False

import pandas as pd 

input = pd.DataFrame([\
                        ["01/01/2019",  1,  43.5863  ,   7.12993,   0],
                        ["01/01/2019",  2,  44.3929  ,   8.93832,   0],
                        ["02/01/2019",  1,  43.5393  ,   7.03134,   1],
                        ["02/01/2019",  2,  39.459462,  -0.31228,   0],
                        ["03/01/2019",  1,  44.3173  ,   84.942,    0],
                        ["03/01/2019",  2,  -12.3284    ,-9.04522,  1],
                        ["04/01/2019",  1,  -36.8414    ,17.4762,   0],
                        ["04/01/2019",  2,  43.542    ,  10.2958,   0],
                        ["05/01/2019",  1,  43.5242  ,   69.473,    0],
                        ["05/01/2019",  2,  37.9382  ,   23.668,    1],
                        ["06/01/2019",  1,  4.4409    ,  89.218,    1],
                        ["06/02/2019",  2,  25.078037,  -77.3289,   0]],
    columns=["date","car_id","latitude",    "longitude" ,   "event"])

input['date'] = pd.to_datetime(input['date'])
df = pd.merge_asof(input.set_index('date'), input.loc[input['event'] == 1].set_index('date'), 
                    on='date', suffixes=['_l','_r'], by='car_id', allow_exact_matches=False)

这时，df中的每一行已经包含了进一步计算所需的必要元素。由于我不确定您的Distance()函数是否接受数据帧，因此我们可以使用.apply()附加distance_since_event列。

def getDistance(lat1, lat2, long1, long2):
    if pd.isna(lat2) or pd.isna(long2):
        return -1
    # substitute this with the actual wgs84_geod library that you eventually use
    return ((lat2-lat1)**2 + (long2-long1)**2) **0.5

df['distance_since_event'] = df.apply(lambda row: getDistance(row['latitude_l'], row['latitude_r'], row['longitude_l'], row['longitude_r']), axis=1)
print(df)

输出：

    car_id       date  latitude_l  longitude_l  event_l  latitude_r  longitude_r  event_r  distance_since_event
0        1 2019-01-01   43.586300      7.12993        0         NaN          NaN      NaN             -1.000000
1        2 2019-01-01   44.392900      8.93832        0         NaN          NaN      NaN             -1.000000
2        1 2019-02-01   43.539300      7.03134        1         NaN          NaN      NaN             -1.000000
3        2 2019-02-01   39.459462     -0.31228        0         NaN          NaN      NaN             -1.000000
4        1 2019-03-01   44.317300     84.94200        0     43.5393      7.03134      1.0             77.914544
5        2 2019-03-01  -12.328400     -9.04522        1         NaN          NaN      NaN             -1.000000
6        1 2019-04-01  -36.841400     17.47620        0     43.5393      7.03134      1.0             81.056474
7        2 2019-04-01   43.542000     10.29580        0    -12.3284     -9.04522      1.0             59.123402
8        1 2019-05-01   43.524200     69.47300        0     43.5393      7.03134      1.0             62.441662
9        2 2019-05-01   37.938200     23.66800        1    -12.3284     -9.04522      1.0             59.974043
10       1 2019-06-01    4.440900     89.21800        1     43.5393      7.03134      1.0             91.012812
11       2 2019-06-02   25.078037    -77.32890        0     37.9382     23.66800      1.0            101.812365

在这里，您可以根据需要重命名或删除列

如何计算每个ID的event == 1以来的覆盖距离

1 个答案: