I applied the following function for each row in my dataframe (df), and using a Lat0/Lon0, direction(azimuth) and distance(dist) I got the new Lat/Lon in "degree minute second":
def location(row):
from geopy.distance import distance
dist = row['dist']
direction = row['azimuth']
lat0 = 20
lon0 = 20
return distance(kilometers=dist).destination((lat0, lon0), direction)
df['coordinate'] = df.apply(lambda row: location(row), axis=1)
I wonder if there is a way to create new two columns in my dataframe with Lat and Lon (“degree decimal”) information based on df['coordinate'] output.
Out[]:
time zenith azimuth o3 uncertainty flag \
61 2019-01-24 15:02:57.983999999 66.90 121.72 241.85 4.9131 1
62 2019-01-24 15:04:35.616000000 66.57 121.94 227.36 4.1773 1
63 2019-01-24 15:06:13.248000000 66.25 122.16 232.97 3.4649 1
64 2019-01-24 15:07:50.880000000 65.92 122.39 236.81 3.1841 1
dist coordinate
61 51.578278 19 45m 16.3524s N, 20 25m 6.9961s E
62 50.766056 19 45m 24.9176s N, 20 24m 39.7557s E
63 49.998803 19 45m 32.885s N, 20 24m 13.9121s E
64 49.227710 19 45m 40.8577s N, 20 23m 47.8847s E
UPDATE Solved
def location(row, lat0, lon0):
from geopy.distance import distance
dist = row['dist']
direction = row['azimuth']
return distance(kilometers=dist).destination((lat0, lon0), direction).format_decimal()
df['coordinate'] = df.apply(lambda row: location(row, 20, 20), axis=1)
split_data = df.coordinate.str.split(', ')
df['lat'] = split_data.apply(lambda x: x[0])
df['long'] = split_data.apply(lambda x: x[1])
Current dataframe:
Out[]:
time zenith azimuth o3 uncertainty flag \
61 2019-01-24 15:02:57.983999999 66.90 121.72 241.85 4.9131 1
62 2019-01-24 15:04:35.616000000 66.57 121.94 227.36 4.1773 1
63 2019-01-24 15:06:13.248000000 66.25 122.16 232.97 3.4649 1
64 2019-01-24 15:07:50.880000000 65.92 122.39 236.81 3.1841 1
dist coordinate lat \
61 51.578278 19.75454233221212, 20.41861002686191 19.75454233221212
62 50.766056 19.756921547635606, 20.411043240303318 19.756921547635606
63 49.998803 19.759134724013204, 20.403864475793757 19.759134724013204
64 49.227710 19.761349364643046, 20.396634631525913 19.761349364643046
long
61 20.41861002686191
62 20.411043240303318
63 20.403864475793757
64 20.396634631525913
If you remove .format_decimal(), your location function would return Point instances instead of strings (see https://geopy.readthedocs.io/en/stable/#geopy.point.Point), where decimal coordinates can easily be extracted as attributes:
df['point'] = df.apply(lambda row: location(row, 20, 20), axis=1)
df['lat'] = df['point'].apply(lambda point: point.latitude)
df['long'] = df['point'].apply(lambda point: point.longitude)