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from functools import partial
from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="manchester_value")
geocode = partial(geolocator.geocode, language="en")
from geopy.geocoders import Nominatim
print(geocode("manchester"))

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#Define Foursquare Credentials and Version

CLIENT_ID = 'Your client id' # Foursquare ID
CLIENT_SECRET = 'Your client secret' # Foursquare Secret
VERSION = '20181206' # Foursquare API version

print('Your credentails:')
print('CLIENT_ID: ' + CLIENT_ID)
print('CLIENT_SECRET:' + CLIENT_SECRET)

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# What are the top 5 venues/facilities nearby profitable real estate investments?#

num_top_venues = 5

for hood in manchester_grouped['Street']:
    print("----"+hood+"----")
    temp = manchester_grouped[manchester_grouped['Street'] == hood].T.reset_index()
    temp.columns = ['venue','freq']
    temp = temp.iloc[1:]
    temp['freq'] = temp['freq'].astype(float)

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#Distribute in 5 Clusters

# set number of clusters
kclusters = 5

manchester_grouped_clustering = manchester_grouped.drop('Street', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(manchester_grouped_clustering)

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# Create Map

map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)

# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i+x+(i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

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manchester_grouped_clustering.loc[manchester_grouped_clustering['Cluster Labels'] == 0, manchester_grouped_clustering.columns[[1] + list(range(5, manchester_grouped_clustering.shape[1]))]].head()

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!pip install --upgrade pip

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import folium 
map_deutschland = folium.Map(location=[51.133481,10.018343], 
                    zoom_start = 5)
map_deutschland

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import json

# word cloud library
from wordcloud import WordCloud

# seaborn
import seaborn as sns

# matplotlib
import matplotlib

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# Plotting a bar graph of the number of stores in each city, for the first ten cities listed# in the column 'City'
city_count  = sbucks_de['City'].value_counts()
city_count = city_count[:10,]
plt.figure(figsize=(10,5))
sns.barplot(city_count.index, city_count.values, alpha=0.8)
plt.title('Starbucks TOP 10 City in DE')
plt.ylabel('Values', fontsize=12)
plt.xlabel('City', fontsize=12)
plt.show()