I have the following code that takes very long time to execute. The pandas DataFrames df
and df_plants
are very small (less than 1Mb). I wonder if there is any way to optimise this code:
import pandas as pd
import geopy.distance
import re
def is_inside_radius(latitude, longitude, df_plants, radius):
if (latitude != None and longitude != None):
lat = float(re.sub("[a-zA-Z]", "", str(latitude)))
lon = float(re.sub("[a-zA-Z]", "", str(longitude)))
for index, row in df_plants.iterrows():
coords_1 = (lat, lon)
coords_2 = (row["latitude"], row["longitude"])
dist = geopy.distance.distance(coords_1, coords_2).km
if dist <= radius:
return 1
return 0
df["inside"] = df.apply(lambda row: is_inside_radius(row["latitude"],row["longitude"],df_plants,10), axis=1)
I use regex to process latitude and longitude in df
because the values contain some errors (characters) which should be deleted.
The function is_inside_radius
verifies if row[latitude]
and row[longitude]
are inside the radius of 10 km from any of the points in df_plants
.
Can you try this?
I've encountered such a problem before, and I see one simple optimisation: try to avoid the floating point calculation as much a possible, which you can do as follows:
Imagine:
You have a circle, defined by Mx and My (center coordinates) and R (radius).
You have a point, defined by is coordinates X and Y.
If your point (X,Y) is not even within the square, defined by (Mx, My) and size 2*R, then it will also not be within the circle, defined by (Mx, My) and radius R.
In pseudo-code: