如何在 python 中按最接近的几何形状和日期和时间合并 2 个数据帧？

Question

我有2个不同的数据框架。在"weather"数据帧有许多几何图形在同一日期和时间。在"road"数据帧有行字符串，日期和时间，但这里它们不是相同的日期或相同的时间，它们都是不同的。

我想做的是合并2数据帧的日期-时间和几何形状。简而言之，我想在相同的日期和时间用最接近的几何图形组合两个数据帧

我试过这样做

1. 首先，我试图从"天气"中找到相同的日期和时间。从"道路"开始日期的数据框。dataframe
2. 然后有多于一个的行和点。我试着找到最近的地方(它在"天气"里)。Dataframe)到行字符串(它在"road"dataframe)
3. 我想做的最后一件事是删除"天气"中的点(删除那些行)dataframe。

我做了一件事，但我猜是错的。我怎样才能解决这个问题?还有别的办法吗?

这是一个"道路"的例子。dataframe:

{{'roadId': {0: 10500,
1: 10500,
2: 10500,
3: 105400,
4: 105400,
5: 106600,
6: 14100,
7: 22800,
8: 22800,
9: 22800,
10: 29000,
11: 31500,
12: 32100,
13: 36400,
14: 36400,
15: 36600,
16: 40400,
17: 40400,
18: 420253800,
19: 420253800,
20: 420253800,
21: 420253800,
22: 420253800,
23: 420253800,
24: 420253800,
25: 420253800,
26: 420253800,
27: 420253800,
28: 46700,
29: 46700,
30: 48600,
31: 48600,
32: 48600,
33: 48600,
34: 48600,
35: 48600,
36: 48600,
37: 48600,
38: 48600,
39: 48600,
40: 48600,
41: 48600,
42: 53700,
43: 60001200,
44: 60002000,
45: 60002000,
46: 60002000,
47: 60002100,
48: 60002100,
49: 60002100},
'date': {0: '2021.03.08 02:00:00',
1: '2021.03.08 05:45:00',
2: '2021.03.08 04:15:00',
3: '2021.03.07 21:30:00',
4: '2021.03.08 05:45:00',
5: '2021.03.08 05:45:00',
6: '2021.03.07 21:30:00',
7: '2021.03.07 21:30:00',
8: '2021.03.08 01:45:00',
9: '2021.03.08 05:45:00',
10: '2021.03.08 05:45:00',
11: '2021.03.08 05:45:00',
12: '2021.03.08 05:45:00',
13: '2021.03.08 00:00:00',
14: '2021.03.08 04:15:00',
15: '2021.03.08 04:15:00',
16: '2021.03.07 21:30:00',
17: '2021.03.08 04:15:00',
18: '2021.03.07 21:15:00',
19: '2021.03.07 20:45:00',
20: '2021.03.07 21:30:00',
21: '2021.03.07 22:30:00',
22: '2021.03.08 00:30:00',
23: '2021.03.08 01:45:00',
24: '2021.03.08 01:45:00',
25: '2021.03.08 02:00:00',
26: '2021.03.08 02:30:00',
27: '2021.03.08 05:45:00',
28: '2021.03.08 04:15:00',
29: '2021.03.08 05:45:00',
30: '2021.03.07 21:30:00',
31: '2021.03.07 22:30:00',
32: '2021.03.08 01:00:00',
33: '2021.03.08 00:30:00',
34: '2021.03.08 03:45:00',
35: '2021.03.08 02:15:00',
36: '2021.03.08 02:00:00',
37: '2021.03.08 02:30:00',
38: '2021.03.08 05:15:00',
39: '2021.03.08 05:00:00',
40: '2021.03.08 05:45:00',
41: '2021.03.08 04:15:00',
42: '2021.03.08 05:45:00',
43: '2021.03.07 22:30:00',
44: '2021.03.07 23:30:00',
45: '2021.03.08 05:45:00',
46: '2021.03.08 04:15:00',
47: '2021.03.07 21:15:00',
48: '2021.03.08 02:30:00',
49: '2021.03.08 05:45:00'},
'lin': {0: 'LINESTRING (32.85619 39.868728, 32.856321 39.868657, 32.856477 39.868615)',
1: 'LINESTRING (32.85619 39.868728, 32.856321 39.868657, 32.856477 39.868615)',
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hava=pd.read_csv("C:/Users

这是一个"天气"的例子。dataframe:

{'Lat': {142761: 40.13,
142762: 40.13,
142763: 40.13,
142764: 40.13,
142765: 40.13,
142766: 40.13,
142767: 40.13,
142768: 40.13,
142769: 40.13,
142770: 40.13,
142771: 40.13,
142772: 40.13,
142773: 40.13,
142774: 40.13,
142775: 40.13,
142776: 40.13,
142777: 40.13,
142778: 40.13,
142779: 40.13,
142780: 40.13,
142781: 40.13,
142782: 40.13,
142783: 40.13,
143474: 39.93,
143475: 39.93,
143476: 39.93,
143477: 39.93,
143478: 39.93,
143479: 39.93,
143480: 39.93,
143481: 39.93,
143482: 39.93,
143483: 39.93,
143484: 39.93,
143485: 39.93,
143486: 39.93,
143487: 39.93,
143488: 39.93,
143489: 39.93,
143490: 39.93,
143491: 39.93,
143492: 39.93,
143493: 39.93,
143494: 39.93,
143495: 39.93,
143496: 39.93,
144187: 39.95,
144188: 39.95,
144189: 39.95,
144190: 39.95},
'Lon': {142761: 33.1,
142762: 33.1,
142763: 33.1,
142764: 33.1,
142765: 33.1,
142766: 33.1,
142767: 33.1,
142768: 33.1,
142769: 33.1,
142770: 33.1,
142771: 33.1,
142772: 33.1,
142773: 33.1,
142774: 33.1,
142775: 33.1,
142776: 33.1,
142777: 33.1,
142778: 33.1,
142779: 33.1,
142780: 33.1,
142781: 33.1,
142782: 33.1,
142783: 33.1,
143474: 32.86,
143475: 32.86,
143476: 32.86,
143477: 32.86,
143478: 32.86,
143479: 32.86,
143480: 32.86,
143481: 32.86,
143482: 32.86,
143483: 32.86,
143484: 32.86,
143485: 32.86,
143486: 32.86,
143487: 32.86,
143488: 32.86,
143489: 32.86,
143490: 32.86,
143491: 32.86,
143492: 32.86,
143493: 32.86,
143494: 32.86,
143495: 32.86,
143496: 32.86,
144187: 32.73,
144188: 32.73,
144189: 32.73,
144190: 32.73},
'Tarih': {142761: '2021.03.08',
142762: '2021.03.08',
142763: '2021.03.08',
142764: '2021.03.08',
142765: '2021.03.08',
142766: '2021.03.08',
142767: '2021.03.08',
142768: '2021.03.08',
142769: '2021.03.08',
142770: '2021.03.08',
142771: '2021.03.08',
142772: '2021.03.08',
142773: '2021.03.08',
142774: '2021.03.08',
142775: '2021.03.08',
142776: '2021.03.08',
142777: '2021.03.08',
142778: '2021.03.08',
142779: '2021.03.08',
142780: '2021.03.08',
142781: '2021.03.08',
142782: '2021.03.08',
142783: '2021.03.08',
143474: '2021.03.08',
143475: '2021.03.08',
143476: '2021.03.08',
143477: '2021.03.08',
143478: '2021.03.08',
143479: '2021.03.08',
143480: '2021.03.08',
143481: '2021.03.08',
143482: '2021.03.08',
143483: '2021.03.08',
143484: '2021.03.08',
143485: '2021.03.08',
143486: '2021.03.08',
143487: '2021.03.08',
143488: '2021.03.08',
143489: '2021.03.08',
143490: '2021.03.08',
143491: '2021.03.08',
143492: '2021.03.08',
143493: '2021.03.08',
143494: '2021.03.08',
143495: '2021.03.08',
143496: '2021.03.08',
144187: '2021.03.08',
144188: '2021.03.08',
144189: '2021.03.08',
144190: '2021.03.08'},
'Saat': {142761: '00:00',
142762: '03:00',
142763: '06:00',
142764: '09:00',
142765: '12:00',
142766: '15:00',
142767: '18:00',
142768: '21:00',
142769: '00:00',
142770: '03:00',
142771: '06:00',
142772: '09:00',
142773: '12:00',
142774: '15:00',
142775: '18:00',
142776: '21:00',
142777: '00:00',
142778: '03:00',
142779: '06:00',
142780: '09:00',
142781: '12:00',
142782: '15:00',
142783: '18:00',
143474: '00:00',
143475: '03:00',
143476: '06:00',
143477: '09:00',
143478: '12:00',
143479: '15:00',
143480: '18:00',
143481: '21:00',
143482: '00:00',
143483: '03:00',
143484: '06:00',
143485: '09:00',
143486: '12:00',
143487: '15:00',
143488: '18:00',
143489: '21:00',
143490: '00:00',
143491: '03:00',
143492: '06:00',
143493: '09:00',
143494: '12:00',
143495: '15:00',
143496: '18:00',
144187: '00:00',
144188: '03:00',
144189: '06:00',
144190: '09:00'},
'Weather': {142761: 1,
142762: 1,
142763: 1,
142764: 1,
142765: 1,
142766: 5,
142767: 5,
142768: 1,
142769: 1,
142770: 1,
142771: 5,
142772: 5,
142773: 11,
142774: 5,
142775: 5,
142776: 1,
142777: 7,
142778: 7,
142779: 4,
142780: 4,
142781: 4,
142782: 1,
142783: 4,
143474: 1,
143475: 1,
143476: 1,
143477: 1,
143478: 5,
143479: 5,
143480: 5,
143481: 1,
143482: 1,
143483: 1,
143484: 1,
143485: 5,
143486: 11,
143487: 5,
143488: 1,
143489: 1,
143490: 7,
143491: 7,
143492: 4,
143493: 4,
143494: 4,
143495: 4,
143496: 4,
144187: 1,
144188: 1,
144189: 1,
144190: 1},
'geometry': {142761: <shapely.geometry.point.Point at 0x1ca1d671948>,
142762: <shapely.geometry.point.Point at 0x1ca1d671b08>,
142763: <shapely.geometry.point.Point at 0x1ca1d671ac8>,
142764: <shapely.geometry.point.Point at 0x1ca1d671a88>,
142765: <shapely.geometry.point.Point at 0x1ca1d6716c8>,
142766: <shapely.geometry.point.Point at 0x1ca1d671808>,
142767: <shapely.geometry.point.Point at 0x1ca1d671788>,
142768: <shapely.geometry.point.Point at 0x1ca1d671908>,
142769: <shapely.geometry.point.Point at 0x1ca1d671248>,
142770: <shapely.geometry.point.Point at 0x1ca1d671388>,
142771: <shapely.geometry.point.Point at 0x1ca1d671c08>,
142772: <shapely.geometry.point.Point at 0x1ca1d671d48>,
142773: <shapely.geometry.point.Point at 0x1ca1d671c88>,
142774: <shapely.geometry.point.Point at 0x1ca1d671688>,
142775: <shapely.geometry.point.Point at 0x1ca1d6717c8>,
142776: <shapely.geometry.point.Point at 0x1ca1d671e88>,
142777: <shapely.geometry.point.Point at 0x1ca1d671988>,
142778: <shapely.geometry.point.Point at 0x1ca1d671e48>,
142779: <shapely.geometry.point.Point at 0x1ca1d671dc8>,
142780: <shapely.geometry.point.Point at 0x1ca1d671d08>,
142781: <shapely.geometry.point.Point at 0x1ca1d671d88>,
142782: <shapely.geometry.point.Point at 0x1ca1d671f48>,
142783: <shapely.geometry.point.Point at 0x1ca1d671b48>,
143474: <shapely.geometry.point.Point at 0x1ca1d6943c8>,
143475: <shapely.geometry.point.Point at 0x1ca1d694548>,
143476: <shapely.geometry.point.Point at 0x1ca1d694388>,
143477: <shapely.geometry.point.Point at 0x1ca1d6942c8>,
143478: <shapely.geometry.point.Point at 0x1ca1d694688>,
143479: <shapely.geometry.point.Point at 0x1ca1d6945c8>,
143480: <shapely.geometry.point.Point at 0x1ca1d694648>,
143481: <shapely.geometry.point.Point at 0x1ca1d694108>,
143482: <shapely.geometry.point.Point at 0x1ca1d6947c8>,
143483: <shapely.geometry.point.Point at 0x1ca1d694488>,
143484: <shapely.geometry.point.Point at 0x1ca1d694788>,
143485: <shapely.geometry.point.Point at 0x1ca1d694708>,
143486: <shapely.geometry.point.Point at 0x1ca1d6948c8>,
143487: <shapely.geometry.point.Point at 0x1ca1d6946c8>,
143488: <shapely.geometry.point.Point at 0x1ca1d694888>,
143489: <shapely.geometry.point.Point at 0x1ca1d694848>,
143490: <shapely.geometry.point.Point at 0x1ca1d6949c8>,
143491: <shapely.geometry.point.Point at 0x1ca1d694808>,
143492: <shapely.geometry.point.Point at 0x1ca1d694748>,
143493: <shapely.geometry.point.Point at 0x1ca1d694b08>,
143494: <shapely.geometry.point.Point at 0x1ca1d694a48>,
143495: <shapely.geometry.point.Point at 0x1ca1d694ac8>,
143496: <shapely.geometry.point.Point at 0x1ca1d694588>,
144187: <shapely.geometry.point.Point at 0x1ca1d6b7408>,
144188: <shapely.geometry.point.Point at 0x1ca1d6b73c8>,
144189: <shapely.geometry.point.Point at 0x1ca1d6b7548>,
144190: <shapely.geometry.point.Point at 0x1ca1d6b7388>}}

下面是我的代码:

liste={}
lis={}
for i in range(road.date.count()):            
for n in range(weather.Tarih.count()):
if (weather.index == (142600+n)).any()==True:
if road.date[i][0:10]==weather.Tarih[142600+n]:
if str(pd.Timestamp(road.date[i][11:16]).round('180min').to_pydatetime())[11:16]==weather.Saat[142600+n]:
point = weather.geometry[142600+n]
liste[142600+n]=point
if len(liste)!=0:
print(liste)
for a, p1 in liste.items():
line = shapely.wkt.loads(road.lin[i])
p2 = nearest_points(line, p1)[0]
distance = math.sqrt(((p1.x-p2.x)**2)+((p1.y-p2.y)**2))
lis[a]=distance
b=min(lis.values())
b=bul(b,lis)
lis.pop(b)
for k in liste.keys():
weather.drop([k], axis=0, inplace=True)
liste.clear()
lis.clear()

此代码用于按日期和时间合并数据帧

road['date'] = pd.to_datetime(road['date'])
road = road.sort_values(by = 'date')
weather['date'] = weather['Tarih'] + ' ' + weather['Saat']
weather['date'] = pd.to_datetime(weather['date'])
weather = weather.sort_values(by = 'date')
newdf = pd.merge_asof(left = road, right = weather, on = 'date', tolerance = pd.Timedelta('180min')) #in weather dataframe there is a difference of 3 hours between the hours like 0-3-6-9

但是当我运行第一段代码然后运行这段代码在某个点之后它变成了nan value

Answer 1

你应该使用direction="nearest"，否则你只会期待

newdf = pd.merge_asof(left = road, right = weather, on = 'date', tolerance = pd.Timedelta('180min'), direction="nearest")

同样，有了这个容差，你无论如何都会有一个nan，但你可以增加它，以去除所有的nans。