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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Showing Berrl Implementation in Jupyter
Step1:  Mapping all fatalities and getting unique hashs for each
Step2:  Showing the new url made with fatalities along certain routes
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Python Code:
import berrl as bl
import numpy as np
import pandas as pd
apikey='pk.eyJ1IjoibXVycGh5MjE0IiwiYSI6ImNpam5kb3puZzAwZ2l0aG01ZW1uMTRjbnoifQ.5Znb4MArp7v3Wwrn6WFE6A'
data=pd.read_csv('wv_traffic_fatals.csv')
#data=data[data.CNTYNAME=='Clay County']
a=bl.make_points(data,list=True)
bl.parselist(a,'fatals.geojson')
# returns url to be used in show() function
url=bl.loadparsehtml(['fatals.geojson'],apikey,frame=True)
bl.show(url)
# getting only the fatalities on certain routes
roadways=bl.get_filetype('roadways','csv')
totaluniques=[]
count=0
filenames=[]
for row in roadways:
    count+=1
    temp=bl.map_table(row,6)
    uniques=np.unique(temp['GEOHASH']).tolist()
    totaluniques+=uniques
    temp['color']='light green'
    a=bl.make_line(temp,list=True)
    bl.parselist(a,str(count)+'.geojson')
    filenames.append(str(count)+'.geojson')
totaluniques=np.unique(totaluniques)
Explanation: Showing Berrl Implementation in Jupyter
End of explanation
# mapping all traffic fatals in WV to a geohash
data=bl.map_table(data,6,list=True)
matched=[]
# getting matching uniques
for row in bl.df2list(data):
    oldrow=row
    for row in totaluniques:
        if oldrow[-1]==row:
            matched.append(row)
# getting point
for row in matched:
    count+=1
    temp=data[data.GEOHASH==row]
    temp['color']='red'
    a=bl.make_points(temp,list=True)
    bl.parselist(a,str(count)+'.geojson')
    filenames.append(str(count)+'.geojson')
newurl=bl.loadparsehtml(filenames,apikey,colorkey='color',frame=True)
Explanation: Mapping all fatalities and getting unique hashs for each
End of explanation
bl.show(newurl)
Explanation: Showing the new url made with fatalities along certain routes
End of explanation
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