用不同DataFrames Python Pandas的经纬度计算Km

Question

I have 4 Dataframes (ticket_data.csv, providers.csv, stations.csv and cities.csv)

In stations.csv I have 2 colls called o_city (origin city) and d_city (destination city) those two colls gives me the id of the city i need to look for in cities.csv

In cities.csv I have the lat and long of each city.

How can i calculate the distance between o_city and d_city for each ticket ? I tried to use pyproj but I didn't find a way to make it work with each ticket..

Screenshot of csv files :

ticket_data.csv

cities.csv

Answer 1

Welcome to StackOverflow! In your cities dataframe, assuming here it is called ; for each row you can use something called the haversine distance formula from Euclidean geometry to calculate the distance between two coordinate pairs on Earth's surface. Here is an example of some dummy Python3 code of roughly how you may go about this (just using two pairs of coordinates for ease of communication):

from haversine import haversine

distance = haversine((city_df[origin_lat][0], city_df[origin_lon][0]), (city_df[destination_lat][0], city_df[destination_lon][0]))

The coordinates must be in decimal degree notation as in 43.9202 instead of 43* 38" 67' notation. Given this, the output value of distance will be in km units.

Hope this helps you get closer to solving your problem!

PS - you may need to install haversine, as it is not in the standard libary