匹配数据框中的行并删除不匹配的行的更快方法是什么？
What is a faster way to match rows in a data frame and remove unmatched rows?
我有一个包含时间、纬度、经度、海拔、速度的数据框，我正在使用 shapely 根据公差来减少数据集以平滑 latitude/longitude 对。它工作正常，但是当我尝试将数据点（纬度、经度）的平滑简化版本与具有时间、高程、元素的原始数据框匹配时，当数据点 > 500 时花费的时间太长。
基本上我所做的是遍历原始数据集并找到匹配对，记录索引直到它们全部匹配。我使用 "last_find" 变量来稍微加快搜索速度，因为这些点几乎总是连续的，没有理由从头开始重新搜索。 FWIW，我从未见过它需要回退到 (last_find=0) 我的测试数据集上的完整数据帧扫描，这根据数据的顺序行性质和平滑方法是有意义的。
lon = pd.Series(pd.Series(simplified_line.coords.xy)[1])
lat = pd.Series(pd.Series(simplified_line.coords.xy)[0])
si = pd.DataFrame({'Longitude': lon, 'Latitude': lat})
si.tail()
si['df_index'] = None
pd.options.mode.chained_assignment = None # default='warn', suppress warning during copying dataframe
last_find = 0 # assume data is sequential and and start search at last point found to reduce iterations
for si_i, si_row in si.iterrows():
si_coords = (si_row['Latitude'], si_row['Longitude'])
found = False
for df_i, df_row in islice(track.iterrows(), last_find, None):
if si_coords == (df_row['Latitude'], df_row['Longitude']):
si['df_index'][si_i] = df_i
last_find = df_i
found = True
break
if not found:
last_find = 0
# Rescanning full dataset for match
for df_i, df_row in islice(track.iterrows(), last_find, None):
if si_coords == (df_row['Latitude'], df_row['Longitude']):
si['df_index'][si_i] = df_i
last_find = df_i
break
rs = track.loc[si['df_index'].dropna()]
将数据帧重建为 "rs" 的过程非常缓慢。 （22 秒仅需 500 分）。有没有更好的方法来进行这种类型的匹配以减少原始数据帧的大小？
这是一个完整的检查示例：
import pandas as pd
from pandas import DataFrame
from shapely.geometry import LineString
from time import time
from itertools import islice
import datetime
class RDP:
def __init__(self, tracks, tolerance=0.000002):
self.df = tracks
self.tolerance = tolerance
return
def smooth(self):
"""
Smooths list of data frames
:return: list of smoothed data frames
"""
results = []
start_time = time()
for track in self.df:
coordinates = track.as_matrix(columns=['Latitude', 'Longitude'])
line = LineString(coordinates)
# If preserve topology is set to False, the method will use the Ramer-Douglas-Peucker algorithm
simplified_line = line.simplify(self.tolerance, preserve_topology=False)
lon = pd.Series(pd.Series(simplified_line.coords.xy)[1])
lat = pd.Series(pd.Series(simplified_line.coords.xy)[0])
si = pd.DataFrame({'Longitude': lon, 'Latitude': lat})
si.tail()
si['df_index'] = None
pd.options.mode.chained_assignment = None # default='warn', suppress warning during copying dataframe
last_find = 0 # assume data is sequential and and start search at last point found to reduce iterations
for si_i, si_row in si.iterrows():
si_coords = (si_row['Latitude'], si_row['Longitude'])
found = False
for df_i, df_row in islice(track.iterrows(), last_find, None):
if si_coords == (df_row['Latitude'], df_row['Longitude']):
si['df_index'][si_i] = df_i
last_find = df_i
found = True
break
if not found:
last_find = 0
# Rescanning full dataset for match
for df_i, df_row in islice(track.iterrows(), last_find, None):
if si_coords == (df_row['Latitude'], df_row['Longitude']):
si['df_index'][si_i] = df_i
last_find = df_i
break
rs = track.loc[si['df_index'].dropna()]
results.append(rs)
print('process took %s seconds' % round(time() - start_time, 2))
return results
if __name__ == "__main__":
data = [[-155.05156, 19.73201, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 9), None, 0],
[-155.05156, 19.73201, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 10), 0.0, 0.0],
[-155.05156, 19.73201, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 11), 1.8244950963755258, 0.0],
[-155.05157, 19.73202, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 12), 1.4678475295952227,
1.527543187532957],
[-155.05157, 19.73203, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 13), 1.11120000035271,
1.1122983328025196],
[-155.05157, 19.73203, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 14), 2.3687194876712123, 0.0],
[-155.05159, 19.73204, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 15), 1.7399596859879076,
2.3710607190787623],
[-155.05159, 19.73205, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 16), 1.7399596281612155,
1.112298332448747],
[-155.05161, 19.73206, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 17), 1.7399595703344959,
2.3710604875433656],
[-155.05161, 19.73207, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 18), 1.7399595111950648,
1.112298332448747],
[-155.05163, 19.73208, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 19), 2.096606870194645,
2.3710602536550747],
[-155.05164, 19.73209, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 20), 1.6752646424182498,
1.527542875149723],
[-155.05165, 19.7321, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 21), 2.289051665826317,
1.5275428299682154],
[-155.05167, 19.73212, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 22), 2.754150510219822,
3.055085523596321],
[-155.05168, 19.73214, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 23), 2.4562322562443732,
2.458660072750598],
[-155.05169, 19.73216, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 24), 2.4124750922364004,
2.458660017743196],
[-155.05171, 19.73217, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 25), 2.9312779133573135,
2.3710592140947706],
[-155.05172, 19.7322, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 26), 2.9312777104723176,
3.497291307909982],
[-155.05174, 19.73221, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 27), 2.7103926533029608,
2.3710587533735854],
[-155.05176, 19.73223, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 28), 2.7541498631496495,
3.0550845355246805],
[-155.05177, 19.73225, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 29), 3.214644630731547,
2.4586597654103666],
[-155.0518, 19.73227, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 30), 3.272104136727811,
3.8489512292988133],
[-155.05182, 19.73228, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 31), 2.4699338807004922,
2.3710579406395524],
[-155.05184, 19.73229, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 32), 2.710391831502253,
2.3710578250365275],
[-155.05186, 19.73231, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 33), 2.571984173054396,
3.055083816479077],
[-155.05188, 19.73231, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 34), 2.2303087951040954,
2.0939690200246193],
[-155.0519, 19.73232, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 35), 2.412473942713288,
2.371057475376574],
[-155.05191, 19.73234, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 36), 2.892513413572397,
2.458659515345401],
[-155.05194, 19.73235, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 37), 3.1904307936770424,
3.3320852845152014],
[-155.05196, 19.73237, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 38), 3.190430517344615,
3.0550832771287606],
[-155.05199, 19.73238, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 39), 3.1904303798788662,
3.332084723430405],
[-155.05201, 19.7324, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 40), 2.710390701811524,
3.055083009665372],
[-155.05203, 19.73241, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 41), 2.3687150928988454,
2.3710564358044426],
[-155.05205, 19.73242, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 42), 2.4699323240615967,
2.371056320034746],
[-155.05207, 19.73243, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 43), 2.4699322129590935,
2.371056201746366],
[-155.05209, 19.73244, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 44), 3.1708845562819272,
2.3710560884951897],
[-155.05212, 19.73246, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 45), 3.1708842854814536,
3.8489481826678027],
[-155.05214, 19.73247, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 46), 2.710389981744121,
2.371055741019484],
[-155.05216, 19.73249, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 47), 2.710389776420281,
3.0550821973706315],
[-155.05218, 19.7325, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 48), 2.710389674730764,
2.3710553940411665],
[-155.0522, 19.73252, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 49), 3.0520651178491267,
3.055081929390566],
[-155.05222, 19.73254, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 50), 2.7103892638045273,
3.0550817499490517],
[-155.05224, 19.73255, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 51), 2.710389160077225,
2.371054817376382],
[-155.05226, 19.73257, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 52), 2.7103889546600244,
3.0550814785741593],
[-155.05228, 19.73258, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 53), 3.1069293021008604,
2.3710544675462026],
[-155.05231, 19.7326, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 54), 2.6855886784059737,
3.8489459341931775],
[-155.05232, 19.73261, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 55), 2.2890482314811806,
1.5275405390797137],
[-155.05234, 19.73263, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 56), 3.0520641294586754,
3.055080939475094],
[-155.05236, 19.73265, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 57), 3.052063950954796,
3.0550807613433832],
[-155.05238, 19.73267, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 58), 3.131887830893072,
3.0550805819008096],
[-155.0524, 19.73269, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 59), 2.79021178709455,
3.0550804024580738],
[-155.05242, 19.7327, 22.0, datetime.datetime(2017, 12, 28, 17, 51), 2.710387619443569, 2.371053080813694],
[-155.05244, 19.73272, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 1), 2.7103874140249378,
3.0550801308237463],
[-155.05246, 19.73273, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 2), 2.790211385044462,
2.3710527309810545],
[-155.05248, 19.73275, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 3), 2.7902111840874317,
3.0550798630984697],
[-155.0525, 19.73276, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 4), 2.7103870033308333,
2.3710523836665467],
[-155.05252, 19.73278, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 5), 3.4486025267361144,
3.0550795919786187],
[-155.05255, 19.7328, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 6), 2.685586626408571,
3.848942726844205],
[-155.05256, 19.73281, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 7), 2.3688709766335045,
1.5275396387506783],
[-155.05258, 19.73283, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 8), 2.790210380257448,
3.055079144293523],
[-155.0526, 19.73284, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 9), 2.7902102788103615,
2.37105145984478],
[-155.05262, 19.73286, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 10), 3.211710268550555,
3.0550788729150917],
[-155.05264, 19.73288, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 11), 3.1318859927435865,
3.0550786952967113],
[-155.05266, 19.7329, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 12), 2.71038556626744,
3.0550785153370557],
[-155.05268, 19.73291, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 13), 2.3687093086562108,
2.3710506470866193],
[-155.0527, 19.73292, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 14), 2.469926768769237,
2.3710505291274866],
[-155.05272, 19.73293, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 15), 3.272095501921749,
2.3710504155416987],
[-155.05275, 19.73295, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 16), 3.170877650790823,
3.8489403198357444],
[-155.05277, 19.73296, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 17), 2.3687087304575547,
2.371050068389868],
[-155.05279, 19.73297, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 18), 2.3687086147991403,
2.3710499502645015],
[-155.05281, 19.73298, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 19), 2.790208873068198,
2.3710498368443993],
[-155.05283, 19.733, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 20), 2.7902086721088404,
3.0550776168243536],
[-155.05285, 19.73301, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 21), 2.3687081533850103,
2.3710494917124847],
[-155.05287, 19.73302, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 22), 2.368708036413647,
2.3710493735868456],
[-155.05289, 19.73303, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 23), 2.71038422971785,
2.3710492556271126],
[-155.05291, 19.73305, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 24), 2.7541450397296856,
3.055077167566475],
[-155.05292, 19.73307, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 25), 2.412468561327511,
2.4586574769885563],
[-155.05294, 19.73308, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 26), 2.368707343773043,
2.371048681300471],
[-155.05296, 19.73309, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 27), 2.7103836137458464,
2.3710485633404104],
[-155.05298, 19.73311, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 28), 2.7103834083234952,
3.0550766266297775],
[-155.053, 19.73312, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 29), 2.710383306631016,
2.3710482158540307],
[-155.05302, 19.73314, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 30), 3.190419933693602,
3.0550763588997274],
[-155.05305, 19.73315, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 31), 2.8487433306642,
3.332070455791797],
[-155.05307, 19.73316, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 32), 2.710382894565474,
2.371047750738638],
[-155.05309, 19.73318, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 33), 2.811600384514776,
3.055075999491225],
[-155.05311, 19.73319, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 34), 2.5711414689310477,
2.3710474054378037],
[-155.05313, 19.7332, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 35), 2.811600187179107,
2.371047289829799],
[-155.05315, 19.73322, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 36), 3.190418832907143,
3.055075638256173],
[-155.05318, 19.73323, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 37), 2.9903853616785745,
3.3320689728999744],
[-155.05319, 19.73325, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 38), 2.5103487005418743,
2.4586569745708617],
[-155.05321, 19.73326, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 39), 2.710381868668547,
2.3710465975375103],
[-155.05323, 19.73328, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 40), 2.7103816619319017,
3.0550751009671164],
[-155.05325, 19.73329, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 41), 2.3687049136083917,
2.3710462453430305],
[-155.05327, 19.7333, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 42), 2.3687047992608443,
2.371046131921171],
[-155.05329, 19.73331, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 43), 2.710381355108257,
2.3710460163125644],
[-155.05331, 19.73333, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 44), 2.7103811483711353,
3.0550746517035354],
[-155.05333, 19.73334, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 45), 2.368704335399346,
2.371045666469919],
[-155.05335, 19.73335, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 46), 3.1069186773136934,
2.3710455533797066],
[-155.05338, 19.73337, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 47), 2.685580778436882,
3.8489335801226137],
[-155.05339, 19.73338, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 48), 2.427402283410334,
1.5275370795403593]]
columns = ['Longitude', 'Latitude', 'Altitude', 'Time', 'Speed',
'Distance']
df = list()
df.append(DataFrame(data, columns=columns))
rdp = RDP(df)
print(rdp.smooth())
最困难的部分是理解您的要求。这等同于从第一个 for 循环开始的所有代码。
rs = si.merge( track, on = ["Latitude", "Longitude"] )
您基本上只是合并基于 2 列的两个数据框。此合并默认为内部合并，它只会保留在两者中都找到匹配项的行。
我有一个包含时间、纬度、经度、海拔、速度的数据框，我正在使用 shapely 根据公差来减少数据集以平滑 latitude/longitude 对。它工作正常，但是当我尝试将数据点（纬度、经度）的平滑简化版本与具有时间、高程、元素的原始数据框匹配时，当数据点 > 500 时花费的时间太长。
基本上我所做的是遍历原始数据集并找到匹配对，记录索引直到它们全部匹配。我使用 "last_find" 变量来稍微加快搜索速度，因为这些点几乎总是连续的，没有理由从头开始重新搜索。 FWIW，我从未见过它需要回退到 (last_find=0) 我的测试数据集上的完整数据帧扫描，这根据数据的顺序行性质和平滑方法是有意义的。
lon = pd.Series(pd.Series(simplified_line.coords.xy)[1])
lat = pd.Series(pd.Series(simplified_line.coords.xy)[0])
si = pd.DataFrame({'Longitude': lon, 'Latitude': lat})
si.tail()
si['df_index'] = None
pd.options.mode.chained_assignment = None # default='warn', suppress warning during copying dataframe
last_find = 0 # assume data is sequential and and start search at last point found to reduce iterations
for si_i, si_row in si.iterrows():
si_coords = (si_row['Latitude'], si_row['Longitude'])
found = False
for df_i, df_row in islice(track.iterrows(), last_find, None):
if si_coords == (df_row['Latitude'], df_row['Longitude']):
si['df_index'][si_i] = df_i
last_find = df_i
found = True
break
if not found:
last_find = 0
# Rescanning full dataset for match
for df_i, df_row in islice(track.iterrows(), last_find, None):
if si_coords == (df_row['Latitude'], df_row['Longitude']):
si['df_index'][si_i] = df_i
last_find = df_i
break
rs = track.loc[si['df_index'].dropna()]
将数据帧重建为 "rs" 的过程非常缓慢。 （22 秒仅需 500 分）。有没有更好的方法来进行这种类型的匹配以减少原始数据帧的大小？
这是一个完整的检查示例：
import pandas as pd
from pandas import DataFrame
from shapely.geometry import LineString
from time import time
from itertools import islice
import datetime
class RDP:
def __init__(self, tracks, tolerance=0.000002):
self.df = tracks
self.tolerance = tolerance
return
def smooth(self):
"""
Smooths list of data frames
:return: list of smoothed data frames
"""
results = []
start_time = time()
for track in self.df:
coordinates = track.as_matrix(columns=['Latitude', 'Longitude'])
line = LineString(coordinates)
# If preserve topology is set to False, the method will use the Ramer-Douglas-Peucker algorithm
simplified_line = line.simplify(self.tolerance, preserve_topology=False)
lon = pd.Series(pd.Series(simplified_line.coords.xy)[1])
lat = pd.Series(pd.Series(simplified_line.coords.xy)[0])
si = pd.DataFrame({'Longitude': lon, 'Latitude': lat})
si.tail()
si['df_index'] = None
pd.options.mode.chained_assignment = None # default='warn', suppress warning during copying dataframe
last_find = 0 # assume data is sequential and and start search at last point found to reduce iterations
for si_i, si_row in si.iterrows():
si_coords = (si_row['Latitude'], si_row['Longitude'])
found = False
for df_i, df_row in islice(track.iterrows(), last_find, None):
if si_coords == (df_row['Latitude'], df_row['Longitude']):
si['df_index'][si_i] = df_i
last_find = df_i
found = True
break
if not found:
last_find = 0
# Rescanning full dataset for match
for df_i, df_row in islice(track.iterrows(), last_find, None):
if si_coords == (df_row['Latitude'], df_row['Longitude']):
si['df_index'][si_i] = df_i
last_find = df_i
break
rs = track.loc[si['df_index'].dropna()]
results.append(rs)
print('process took %s seconds' % round(time() - start_time, 2))
return results
if __name__ == "__main__":
data = [[-155.05156, 19.73201, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 9), None, 0],
[-155.05156, 19.73201, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 10), 0.0, 0.0],
[-155.05156, 19.73201, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 11), 1.8244950963755258, 0.0],
[-155.05157, 19.73202, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 12), 1.4678475295952227,
1.527543187532957],
[-155.05157, 19.73203, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 13), 1.11120000035271,
1.1122983328025196],
[-155.05157, 19.73203, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 14), 2.3687194876712123, 0.0],
[-155.05159, 19.73204, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 15), 1.7399596859879076,
2.3710607190787623],
[-155.05159, 19.73205, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 16), 1.7399596281612155,
1.112298332448747],
[-155.05161, 19.73206, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 17), 1.7399595703344959,
2.3710604875433656],
[-155.05161, 19.73207, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 18), 1.7399595111950648,
1.112298332448747],
[-155.05163, 19.73208, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 19), 2.096606870194645,
2.3710602536550747],
[-155.05164, 19.73209, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 20), 1.6752646424182498,
1.527542875149723],
[-155.05165, 19.7321, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 21), 2.289051665826317,
1.5275428299682154],
[-155.05167, 19.73212, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 22), 2.754150510219822,
3.055085523596321],
[-155.05168, 19.73214, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 23), 2.4562322562443732,
2.458660072750598],
[-155.05169, 19.73216, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 24), 2.4124750922364004,
2.458660017743196],
[-155.05171, 19.73217, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 25), 2.9312779133573135,
2.3710592140947706],
[-155.05172, 19.7322, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 26), 2.9312777104723176,
3.497291307909982],
[-155.05174, 19.73221, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 27), 2.7103926533029608,
2.3710587533735854],
[-155.05176, 19.73223, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 28), 2.7541498631496495,
3.0550845355246805],
[-155.05177, 19.73225, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 29), 3.214644630731547,
2.4586597654103666],
[-155.0518, 19.73227, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 30), 3.272104136727811,
3.8489512292988133],
[-155.05182, 19.73228, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 31), 2.4699338807004922,
2.3710579406395524],
[-155.05184, 19.73229, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 32), 2.710391831502253,
2.3710578250365275],
[-155.05186, 19.73231, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 33), 2.571984173054396,
3.055083816479077],
[-155.05188, 19.73231, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 34), 2.2303087951040954,
2.0939690200246193],
[-155.0519, 19.73232, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 35), 2.412473942713288,
2.371057475376574],
[-155.05191, 19.73234, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 36), 2.892513413572397,
2.458659515345401],
[-155.05194, 19.73235, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 37), 3.1904307936770424,
3.3320852845152014],
[-155.05196, 19.73237, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 38), 3.190430517344615,
3.0550832771287606],
[-155.05199, 19.73238, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 39), 3.1904303798788662,
3.332084723430405],
[-155.05201, 19.7324, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 40), 2.710390701811524,
3.055083009665372],
[-155.05203, 19.73241, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 41), 2.3687150928988454,
2.3710564358044426],
[-155.05205, 19.73242, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 42), 2.4699323240615967,
2.371056320034746],
[-155.05207, 19.73243, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 43), 2.4699322129590935,
2.371056201746366],
[-155.05209, 19.73244, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 44), 3.1708845562819272,
2.3710560884951897],
[-155.05212, 19.73246, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 45), 3.1708842854814536,
3.8489481826678027],
[-155.05214, 19.73247, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 46), 2.710389981744121,
2.371055741019484],
[-155.05216, 19.73249, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 47), 2.710389776420281,
3.0550821973706315],
[-155.05218, 19.7325, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 48), 2.710389674730764,
2.3710553940411665],
[-155.0522, 19.73252, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 49), 3.0520651178491267,
3.055081929390566],
[-155.05222, 19.73254, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 50), 2.7103892638045273,
3.0550817499490517],
[-155.05224, 19.73255, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 51), 2.710389160077225,
2.371054817376382],
[-155.05226, 19.73257, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 52), 2.7103889546600244,
3.0550814785741593],
[-155.05228, 19.73258, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 53), 3.1069293021008604,
2.3710544675462026],
[-155.05231, 19.7326, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 54), 2.6855886784059737,
3.8489459341931775],
[-155.05232, 19.73261, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 55), 2.2890482314811806,
1.5275405390797137],
[-155.05234, 19.73263, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 56), 3.0520641294586754,
3.055080939475094],
[-155.05236, 19.73265, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 57), 3.052063950954796,
3.0550807613433832],
[-155.05238, 19.73267, 23.0, datetime.datetime(2017, 12, 28, 17, 50, 58), 3.131887830893072,
3.0550805819008096],
[-155.0524, 19.73269, 22.0, datetime.datetime(2017, 12, 28, 17, 50, 59), 2.79021178709455,
3.0550804024580738],
[-155.05242, 19.7327, 22.0, datetime.datetime(2017, 12, 28, 17, 51), 2.710387619443569, 2.371053080813694],
[-155.05244, 19.73272, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 1), 2.7103874140249378,
3.0550801308237463],
[-155.05246, 19.73273, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 2), 2.790211385044462,
2.3710527309810545],
[-155.05248, 19.73275, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 3), 2.7902111840874317,
3.0550798630984697],
[-155.0525, 19.73276, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 4), 2.7103870033308333,
2.3710523836665467],
[-155.05252, 19.73278, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 5), 3.4486025267361144,
3.0550795919786187],
[-155.05255, 19.7328, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 6), 2.685586626408571,
3.848942726844205],
[-155.05256, 19.73281, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 7), 2.3688709766335045,
1.5275396387506783],
[-155.05258, 19.73283, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 8), 2.790210380257448,
3.055079144293523],
[-155.0526, 19.73284, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 9), 2.7902102788103615,
2.37105145984478],
[-155.05262, 19.73286, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 10), 3.211710268550555,
3.0550788729150917],
[-155.05264, 19.73288, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 11), 3.1318859927435865,
3.0550786952967113],
[-155.05266, 19.7329, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 12), 2.71038556626744,
3.0550785153370557],
[-155.05268, 19.73291, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 13), 2.3687093086562108,
2.3710506470866193],
[-155.0527, 19.73292, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 14), 2.469926768769237,
2.3710505291274866],
[-155.05272, 19.73293, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 15), 3.272095501921749,
2.3710504155416987],
[-155.05275, 19.73295, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 16), 3.170877650790823,
3.8489403198357444],
[-155.05277, 19.73296, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 17), 2.3687087304575547,
2.371050068389868],
[-155.05279, 19.73297, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 18), 2.3687086147991403,
2.3710499502645015],
[-155.05281, 19.73298, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 19), 2.790208873068198,
2.3710498368443993],
[-155.05283, 19.733, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 20), 2.7902086721088404,
3.0550776168243536],
[-155.05285, 19.73301, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 21), 2.3687081533850103,
2.3710494917124847],
[-155.05287, 19.73302, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 22), 2.368708036413647,
2.3710493735868456],
[-155.05289, 19.73303, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 23), 2.71038422971785,
2.3710492556271126],
[-155.05291, 19.73305, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 24), 2.7541450397296856,
3.055077167566475],
[-155.05292, 19.73307, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 25), 2.412468561327511,
2.4586574769885563],
[-155.05294, 19.73308, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 26), 2.368707343773043,
2.371048681300471],
[-155.05296, 19.73309, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 27), 2.7103836137458464,
2.3710485633404104],
[-155.05298, 19.73311, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 28), 2.7103834083234952,
3.0550766266297775],
[-155.053, 19.73312, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 29), 2.710383306631016,
2.3710482158540307],
[-155.05302, 19.73314, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 30), 3.190419933693602,
3.0550763588997274],
[-155.05305, 19.73315, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 31), 2.8487433306642,
3.332070455791797],
[-155.05307, 19.73316, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 32), 2.710382894565474,
2.371047750738638],
[-155.05309, 19.73318, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 33), 2.811600384514776,
3.055075999491225],
[-155.05311, 19.73319, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 34), 2.5711414689310477,
2.3710474054378037],
[-155.05313, 19.7332, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 35), 2.811600187179107,
2.371047289829799],
[-155.05315, 19.73322, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 36), 3.190418832907143,
3.055075638256173],
[-155.05318, 19.73323, 23.0, datetime.datetime(2017, 12, 28, 17, 51, 37), 2.9903853616785745,
3.3320689728999744],
[-155.05319, 19.73325, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 38), 2.5103487005418743,
2.4586569745708617],
[-155.05321, 19.73326, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 39), 2.710381868668547,
2.3710465975375103],
[-155.05323, 19.73328, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 40), 2.7103816619319017,
3.0550751009671164],
[-155.05325, 19.73329, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 41), 2.3687049136083917,
2.3710462453430305],
[-155.05327, 19.7333, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 42), 2.3687047992608443,
2.371046131921171],
[-155.05329, 19.73331, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 43), 2.710381355108257,
2.3710460163125644],
[-155.05331, 19.73333, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 44), 2.7103811483711353,
3.0550746517035354],
[-155.05333, 19.73334, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 45), 2.368704335399346,
2.371045666469919],
[-155.05335, 19.73335, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 46), 3.1069186773136934,
2.3710455533797066],
[-155.05338, 19.73337, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 47), 2.685580778436882,
3.8489335801226137],
[-155.05339, 19.73338, 22.0, datetime.datetime(2017, 12, 28, 17, 51, 48), 2.427402283410334,
1.5275370795403593]]
columns = ['Longitude', 'Latitude', 'Altitude', 'Time', 'Speed',
'Distance']
df = list()
df.append(DataFrame(data, columns=columns))
rdp = RDP(df)
print(rdp.smooth())
最困难的部分是理解您的要求。这等同于从第一个 for 循环开始的所有代码。
rs = si.merge( track, on = ["Latitude", "Longitude"] )
您基本上只是合并基于 2 列的两个数据框。此合并默认为内部合并，它只会保留在两者中都找到匹配项的行。