I have a data frame having two columns latitude and longitude, and 863 rows so that each row has a point coordinate defined by latitude and longitude. Now I want to calculate the distance between all the rows in kilometers. I am using the following reference link to obtain the distance between latitude and longitude pair. If there were a few rows, I could have done using the reference link. But I have large rows and I think I need a loop to achieve a solution to the problem. Since I am new to python I couldn't able to create a logic to looping this idea.
Reference link: Getting distance between two points based on latitude/longitude
My data frame looks like this:
read_randomly_generated_lat_lon.head(3)
Lat Lon
43.937845 -97.905537
44.310739 -97.588820
44.914698 -99.003517
Please note: The following script does not account for the curvature of the earth. There are numerous documents Convert lat/long to XY explaining this problem.
However, the distance between coordinates can be roughly determined. The export is a Series, which can be easily concatenated with your original df to provide a separate column displaying distance relative to your coordinates.
d = ({
'Lat' : [43.937845,44.310739,44.914698],
'Long' : [-97.905537,-97.588820,-99.003517],
})
df = pd.DataFrame(d)
df = df[['Lat','Long']]
point1 = df.iloc[0]
def to_xy(point):
r = 6371000 #radians of the earth (m)
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)
df['to_xy'] = df.apply(lambda x:
tuple(x.values),
axis=1).map(to_xy)
df['Y'], df['X'] = df.to_xy.str[0], df.to_xy.str[1]
df = df[['X','Y']]
df = df.diff()
dist = np.sqrt(df['X']**2 + df['Y']**2)
#Convert to km
dist = dist/1000
print(dist)
0 NaN
1 41.149537
2 204.640462