pip install wordcloud

#import pickle
#data1="some data"
#pickle.dump(data, open('C:\\Users\\pi\\Desktop\\Numpy\\biolocation.txt', 'wb'))

DATA_PATH = ("C:\\Users\\pi\\Desktop\\Numpy\\biolocation.txt")
data = np.load(DATA_PATH,allow_pickle=True)
sorted_array = np.sort(data, axis=None)
#sorted_array = np.ndarray.sort(data)
print(sorted_array)
#nnp= np.genfromtxt('C:\\Users\\pi\\Desktop\\Numpy\\biolocation.txt', encoding='utf-8')

import geopy
from geopy.geocoders import Nominatim
from geopy.extra.rate_limiter import RateLimiter
import pandas as pd
#from pandas import DataFrame

df = pd.DataFrame({'name':['paris', 'berlin', 'london', 'lagos'], 
                   'fullcountry':['France', 'Germany', 'England', 'Nigeria']})

gf = Nominatim(user_agent="C:\\Users\\pi\\Desktop\\Numpy\\biolocation.txt")

geocode = RateLimiter(gf.geocode, min_delay_seconds=1)
df['location'] = df['name'].apply(geocode)
df['country'] = df['fullcountry'].apply(geocode)

import re
with open("C:\\Users\\pi\\Desktop\\Numpy\\biolocation.txt", 'r', encoding='utf-8') as f:       #encoding='cp856')
    data = f.read()
m = re.findall('(\w+)\n(\w+)', data)
d = {'Country':[c[0] for c in m], 'Postion': [c[1] for c in m] }
df = pd.DataFrame(data=d)
print(df)

#df = pd.read_table("C:\\Users\\pi\\Desktop\\Numpy\\biolocation.txt")b
#print(df)

for name in dir(geopy):
    print(name)