Crafting Complexity: Voronoi Polygons Made Simple with Python!

So i share my solution that probably will help.

#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.spatial import Voronoi, voronoi_plot_2d

#Importing the dataset
sites=pd.read_excel('coordinates.xlsx')
y = sites [['LATITUDE', 'LONGITUDE']]


#Drawing polygons
vor = Voronoi(y)
fig = voronoi_plot_2d(vor)
plt,show()

#getting sites coordinates for all polygons
dfp3 =pd.DataFrame(columns=['REGION'] )
RNG2=len(vor.points)-1
for i in range(0,RNG2):
    dfp = pd.DataFrame([vor.point_region[i]],columns=['REGION'])
    dfp.insert(1,'LATITUDE',vor.points[i][0])
    dfp.insert(2,'LONGITUDE',vor.points[i][1])
    dfp2=dfp
    frames = [dfp3, dfp2]
    dfp3 = pd.concat(frames)
dfp3 
# Getting polygons GEO coordinates
# Correcting GEO coordinates if needed - depends of the task you solve, maybe you can skip correction step
RNG=len(vor.regions)-1
df3 =pd.DataFrame(columns=['VORONOY'] )
df2 =pd.DataFrame(columns=['REGION', 'VORONOY'] )
for i in range(0,RNG):
    
    RNG1=len(vor.regions[i])
    if RNG1>0 and (-1 not in vor.regions[i]):
        
        s=''
        for k in range(0,RNG1):
            lo=vor.vertices[vor.regions[i]][k][1]
            lon =lo  if abs(lo)<180.00000000000000 else 180.00000000000000
            la=vor.vertices[vor.regions[i]][k][0]
            lat =la  if abs(la)<90.00000000000000 else 90.00000000000000
            s1=str(lon)+' '+str(lat)
            s=s+','+s1

        lo1=vor.vertices[vor.regions[i]][0][1]
        lon1 =lo1  if abs(lo1)<180.00000000000000 else 180.00000000000000
        la1=vor.vertices[vor.regions[i]][0][0]
        lat1 =la1  if abs(la1)<90.00000000000000 else 90.00000000000000
        s2=str(lon1)+' '+str(lat1) 
        s3=s+','+s2
        s3=s3[1:]

        df = pd.DataFrame([i],columns=['REGION'])
        df.insert(1,'VORONOY',s3)
        df2=df
        
    else:
        pass 

    frames = [df3, df2]
    df3 = pd.concat(frames)
# Joining sites to polygons    
df3 = df3.drop_duplicates(subset=['VORONOY'])
result1 = pd.merge(dfp3, df3, on=["REGION"])
result = pd.merge(sites, result1, on=["LATITUDE", "LONGITUDE"])
result = result.drop_duplicates(subset=['PLACE_NAME'])
# result = result.reset_index()
result


#Exporting resulting table as XLSX
result.to_excel('voronoi_results.xlsx')