将纬度/经度转换为XY的有效方法

Question

I have a working script that converts Latitude and Longitude coordinates to Cartesian coordinates. However, I have to perform this for specific points at each point in time ( row by row ).

I want to do something similar on a larger df . I'm not sure if a loop that iterates over each row is the most efficient way to do this? Below is the script that converts a single XY point.

import math
import numpy as np
import pandas as pd

point1 = [-37.83028766, 144.9539561]

r = 6371000 #radians of earth meters

phi_0 = point1[1]
cos_phi_0 = math.cos(np.radians(phi_0))

def to_xy(point, r, cos_phi_0):
    lam = point[0]
    phi = point[1]
    return (r * np.radians(lam) * cos_phi_0, r * np.radians(phi))

point1_xy = to_xy(point1, r, cos_phi_0)

This works fine if I want to convert between single points. The issue is if I have a large data frame or list ( >100,000 rows ) of coordinates. Would a loop that iterates through each row be inefficient. Is there a better way to perform the same function?

Below is an example of a fractionally bigger df .

d = ({
    'Time' : [0,1,2,3,4,5,6,7,8],       
    'Lat' : [37.8300,37.8200,37.8200,37.8100,37.8000,37.8000,37.7900,37.7900,37.7800],       
    'Long' : [144.8500,144.8400,144.8600,144.8700,144.8800,144.8900,144.8800,144.8700,144.8500],                               
     })

df = pd.DataFrame(data = d)

Answer 1

I will do this if I were you. (Btw: the tuple casting part can be optimized.

import numpy as np
import pandas as pd

point1 = [-37.83028766, 144.9539561]

def to_xy(point):

    r = 6371000  #radians of earth meters
    lam,phi = point
    cos_phi_0 = np.cos(np.radians(phi))


    return (r * np.radians(lam) * cos_phi_0,
            r * np.radians(phi))

point1_xy = to_xy(point1)
print(point1_xy)

d = ({
    'Lat' : [37.8300,37.8200,37.8200,37.8100,37.8000,37.8000,37.7900,37.7900,37.7800],       
    'Long' : [144.8500,144.8400,144.8600,144.8700,144.8800,144.8900,144.8800,144.8700,144.8500],                               
     })

df = pd.DataFrame(d)

df['to_xy'] = df.apply(lambda x: 
         tuple(x.values),
         axis=1).map(to_xy)

print(df)