【发布时间】：2021-11-14 15:15:23
【问题描述】：
我一直在关注这个tutorial，了解如何使用 scikit 找到一个点的最近邻居。
但是，在显示数据时，教程只提到“索引可以映射到有用的值，并且两个数组与其余数据合并”
但是没有关于如何执行此操作的实际解释。我对 Pandas 不是很精通，也不知道如何执行此合并，所以我最终得到了 2 个多维数组，我不知道如何将它们映射到原始数据以研究示例和尝试一下。
这是代码
import numpy as np
from sklearn.neighbors import BallTree, KDTree
import pandas as pd
# Column names for the example DataFrame.
column_names = ["STATION NAME", "LAT", "LON"]
# A list of locations that will be used to construct the binary
# tree.
locations_a = [['BEAUFORT', 32.4, -80.633],
['CONWAY HORRY COUNTY AIRPORT', 33.828, -79.122],
['HUSTON/EXECUTIVE', 29.8, -95.9],
['ELIZABETHTON MUNI', 36.371, -82.173],
['JACK BARSTOW AIRPORT', 43.663, -84.261],
['MARLBORO CO JETPORT H E AVENT', 34.622, -79.734],
['SUMMERVILLE AIRPORT', 33.063, -80.279]]
# A list of locations that will be used to construct the queries.
# for neighbors.
locations_b = [['BOOMVANG HELIPORT / OIL PLATFORM', 27.35, -94.633],
['LEE COUNTY AIRPORT', 36.654, -83.218],
['ELLINGTON', 35.507, -86.804],
['LAWRENCEVILLE BRUNSWICK MUNI', 36.773, -77.794],
['PUTNAM CO', 39.63, -86.814]]
# Converting the lists to DataFrames. We will build the tree with
# the first and execute the query on the second.
locations_a = pd.DataFrame(locations_a, columns = column_names)
locations_b = pd.DataFrame(locations_b, columns = column_names)
# Creates new columns converting coordinate degrees to radians.
for column in locations_a[["LAT", "LON"]]:
rad = np.deg2rad(locations_a[column].values)
locations_a[f'{column}_rad'] = rad
for column in locations_b[["LAT", "LON"]]:
rad = np.deg2rad(locations_b[column].values)
locations_b[f'{column}_rad'] = rad
# Takes the first group's latitude and longitude values to construct
# the ball tree.
ball = BallTree(locations_a[["LAT_rad", "LON_rad"]].values, metric='haversine')
# The amount of neighbors to return.
k = 1
# Executes a query with the second group. This will also return two
# arrays.
distances, indices = ball.query(locations_b[["LAT_rad", "LON_rad"]].values, k = k)
#converting to kilometers
distances = distances * 6.371
那么我该如何获取distances 和indices 并将它们映射到我的数据框以直观地查看每个点的最近邻居？
【问题讨论】：
-
注意：对于以公里为单位的距离，请从
distances = distances * 6.371中删除.
标签： python pandas dataframe numpy scikit-learn