10_top_ten_venues.py

def return_most_common_venues(row, num_top_venues):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_venues]
  
  num_top_venues = 12

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

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

# create a new data frame
Neighbourhoods_venues_sorted = pd.DataFrame(columns=columns)
Neighbourhoods_venues_sorted['Neighbourhood'] = la_grouped['Neighbourhood']

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

Neighbourhoods_venues_sorted.head()