是否有更快的方法（在带CPU的Python中）与下面的函数做相同的事情？我使用过For循环和if语句，想知道是否有更快的方法？目前，每100个邮政编码大约需要1分钟才能运行此功能，而我大约有70,000个要通过。
使用的2个数据帧是：
postcode_df 其中包含71,092行和列：
例如
postcode_df = pd.DataFrame({"Postcode":["SK12 2LH", "SK7 6LQ"],
"Latitude":[53.362549, 53.373812],
"Longitude":[-2.061329, -2.120956]})
air 其中包含421行和列：
例如
air = pd.DataFrame({"TubeRef":["Stkprt35", "Stkprt07", "Stkprt33"],
"Latitude":[53.365085, 53.379502, 53.407510],
"Longitude":[-2.0763, -2.120777, -2.145632]})
该函数循环遍历postcode_df中的每个邮政编码，并且对于每个邮政编码循环遍历每个TubeRef并计算（使用geopy）它们之间的距离，并以距邮政编码最短的距离保存TubeRef。
输出df postcode_nearest_tube_refs包含每个邮政编码最近的试管，并包含以下列：
# define function to get nearest air quality monitoring tube per postcode
def get_nearest_tubes(constituency_list):
postcodes = []
nearest_tubes = []
distances_to_tubes = []
for postcode in postcode_df["Postcode"]:
closest_tube = ""
shortest_dist = 500
postcode_lat = postcode_df.loc[postcode_df["Postcode"]==postcode, "Latitude"]
postcode_long = postcode_df.loc[postcode_df["Postcode"]==postcode, "Longitude"]
postcode_coord = (float(postcode_lat), float(postcode_long))
for tuberef in air["TubeRef"]:
tube_lat = air.loc[air["TubeRef"]==tuberef, "Latitude"]
tube_long = air.loc[air["TubeRef"]==tuberef, "Longitude"]
tube_coord = (float(tube_lat), float(tube_long))
# calculate distance between postcode and tube
dist_to_tube = geopy.distance.distance(postcode_coord, tube_coord).km
if dist_to_tube < shortest_dist:
shortest_dist = dist_to_tube
closest_tube = str(tuberef)
# save postcode's tuberef with shortest distance
postcodes.append(str(postcode))
nearest_tubes.append(str(closest_tube))
distances_to_tubes.append(shortest_dist)
# create dataframe of the postcodes, nearest tuberefs and distance
postcode_nearest_tube_refs = pd.DataFrame({"Postcode":postcodes,
"Nearest Air Tube":nearest_tubes,
"Distance to Air Tube KM": distances_to_tubes})
return postcode_nearest_tube_refs
我正在使用的库是：
import numpy as np
import pandas as pd
# !pip install geopy
import geopy.distance
这里的一个工作示例需要花费几秒钟（<10）。
导入库
import pandas as pd
import numpy as np
from sklearn.neighbors import BallTree
import uuid
我生成一些随机数据，这也需要一秒钟，但是至少我们有一些实际的数量。
np_rand_post = 5 * np.random.random((72000,2))
np_rand_post = np_rand_post + np.array((53.577653, -2.434136))
并将UUID用于伪造的邮政编码
postcode_df = pd.DataFrame( np_rand_post , columns=['lat', 'long'])
postcode_df['postcode'] = [uuid.uuid4().hex[:6] for _ in range(72000)]
postcode_df.head()
我们对空气也是如此
np_rand = 5 * np.random.random((500,2))
np_rand = np_rand + np.array((53.55108, -2.396236))
并再次使用uuid进行伪引用
tube_df = pd.DataFrame( np_rand , columns=['lat', 'long'])
tube_df['ref'] = [uuid.uuid4().hex[:5] for _ in range(500)]
tube_df.head()
将gps值提取为numpy
postcode_gps = postcode_df[["lat", "long"]].values
air_gps = tube_df[["lat", "long"]].values
创建一棵球树
postal_radians = np.radians(postcode_gps)
air_radians = np.radians(air_gps)
tree = BallTree(air_radians, leaf_size=15, metric='haversine')
查询最接近的第一
distance, index = tree.query(postal_radians, k=1)
请注意，距离不是以KM为单位，您需要先进行转换。
earth_radius = 6371000
distance_in_meters = distance * earth_radius
distance_in_meters
例如用 tube_df.ref[ index[:,0] ]
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