I have a dataframe (pd) where each row contains a bunch of measures, as well as latitude
and longitude
values. I can convert those into geopandas points if needed.
From this dataframe, I would like to select only rows that fall within a certain (let's say 1km) radius from a new given lat/long.
Is there a wise way to go about this problem?
Here's a data sample from the df:
id . lat . long . polution . label
----------------------------------------
3 . 45.467. -79.51 . 7 . 'nice'
7 . 45.312. -79.56 . 8 . 'mediocre'
a sample lat/long would be lat = 45.4
and long = -79.5
.
Here's an example of working code. First make a function to calculate your distance. I implemented a simple distance calculation, but I would recommending which ever you feel most useful. Next you can subset the DataFrame to be within your desired distance.
#Initialize DataFrame
df=pd.DataFrame(columns=['location','lat','lon'])
df['location']=['LA','NY','LV']
df['lat']=[34.05,40.71,36.16]
df['lon']=[-118.24,-74.00,-115.14]
#New point Reno 39.53,-119.81
newlat=39.53
newlon=-119.81
#Import trig stuff from math
from math import sin, cos, sqrt, atan2,radians
#Distance function between two lat/lon
def getDist(lat1,lon1,lat2,lon2):
R = 6373.0
lat1 = radians(lat1)
lon1 = radians(lon1)
lat2 = radians(lat2)
lon2 = radians(lon2)
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
return R * c
#Apply distance function to dataframe
df['dist']=list(map(lambda k: getDist(df.loc[k]['lat'],df.loc[k]['lon'],newlat,newlon), df.index))
#This will give all locations within radius of 600 km
df[df['dist']<600]
You can use the following algorithm:
Create a geodataframe ( gdfdata
) from the input data (pd dataframe)
Create another geodataframe ( gdfsel
) with the center point for the selection
Create a buffer around the center point (make gdfselbuff
from gdfsel
) for the selection
Use the within
method of geopandas to find the points within. Eg gdf_within = gdfdata.loc[gdfdata.geometry.within(gdfselbuff.unary_union)]
For making the buffer, you can use GeoSeries.buffer(distance, resolution))
. See these links for reference.
On top of Sharder's solution, I found convenient to apply a filter function. It also seems to execute faster
def filter(row,lat2,lon2,max):
if getDist(row['lat'],row['lon'],lat2,lon2) < max:
return True
else:
return False
df[df.apply(filter, args = (newlat,newlon,600), axis=1)]
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