在查找 Lat 和 Long 之间的距离时维护标识符

Question

我有两组纬度和经度，我希望通过笛卡尔连接来加入并找到每对之间的距离。可以在number或other_number中重复（即每个标识符两个locations/addresses）。

d = {'number': ['100', '101'], 'lat': ['40.6892', '41.8902'], 'long': ['74.0445','12.4922']}
d2 = {'other_number': ['200', '201'], 'lat': ['37.8199', '43.8791'], 'long': ['122.4783','103.4591']}
data = pd.DataFrame(data=d)
data2 = pd.DataFrame(data=d2)

我目前正在将 lat/long 字段转换为元组列表...

tuple_list_1 = list(zip(data.lat.astype(float), data.long.astype(float)))
tuple_list_2 = list(zip(data2.lat.astype(float), data2.long.astype(float)))

...然后使用生成器执行笛卡尔连接。

gen = ([x, y] for x in tuple_list_1 for y in tuple_list_2)

最后，我用一个简单的循环找到了距离：

from geopy.distance import geodesic

for u, v in gen:
    dist = geodesic(u, v).miles
    print(dist)

最终，我希望将距离与原始信息联系起来（即 number 和 other_number）。这是我想要的结果：

    d3 = {'number': ['100', '100','100','100'], 
     'address': ['Statue of Liberty', 'Statue of Liberty', 'Colosseum', 'Colosseum'],
     'other_number': ['200', '200', '201', '201'],
     'other_address': ['Golden Gate Bridge','Mount Rushmore','Golden Gate Bridge','Mount Rushmore'],
     'distance':[2572.262967759492,1515.3455804766047,5400.249562015358,4365.4386483486205]
    }
data3 = pd.DataFrame(data=d3)

如何有效地检索距离（我认为遍历生成器可能效率不高），并将结果与最终 DataFrame 中的标识字段联系起来？

Answer 1

import pandas as pd

d = {'number': ['100', '101'], 'lat': ['40.6892', '41.8902'], 'long': ['74.0445','12.4922']}
d2 = {'other_number': ['200', '201'], 'lat': ['37.8199', '43.8791'], 'long': ['122.4783','103.4591']}
data = pd.DataFrame(data=d)
data2 = pd.DataFrame(data=d2)

# Perform cartesian product
data['key'] = 0
data2['key'] = 0
df = pd.merge(data, data2, on='key', how='outer')
df = df.drop('key', axis=1)

# Calculate distance
from geopy.distance import geodesic
df['distance'] = df.apply(lambda row: geodesic((row['lat_x'], row['long_x']), (row['lat_y'], row['long_y'])).miles, axis=1)

df 看起来像这样：

  number    lat_x   long_x other_number    lat_y    long_y     distance
0    100  40.6892  74.0445          200  37.8199  122.4783  2572.262968
1    100  40.6892  74.0445          201  43.8791  103.4591  1515.345580
2    101  41.8902  12.4922          200  37.8199  122.4783  5400.249562
3    101  41.8902  12.4922          201  43.8791  103.4591  4365.438648

在 pandas 中还有其他执行笛卡尔积的方法，如果您不喜欢这个通过新的 key 列，请参阅 cartesian product in pandas。

在查找 Lat 和 Long 之间的距离时维护标识符

Maintain Identifiers While Finding Distance Between Lat and Long

python

geopy

python-3.x

pandas