top_venues.py

num_top_venues = 10

indicators = ['st', 'nd', 'rd']

# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
    try:
        # append 'st', 'nd', 'rd' to the top 3 venues
        columns.append('{}{} Most Common Venue'.format(ind + 1, indicators[ind]))
    except:
        columns.append('{}th Most Common Venue'.format(ind + 1))

# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns = columns)
neighborhoods_venues_sorted['Neighborhood'] = sf_grouped['Neighborhood']

for ind in np.arange(sf_grouped.shape[0]):
    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(sf_grouped.iloc[ind, :], num_top_venues)

neighborhoods_venues_sorted.head()