<jupyter_start><jupyter_text>1. Get variance of prices<jupyter_code>def listings_price_var(calendars):
    listings_var = {}
    for i in parsed_calendars.keys():
        listings_var[i] = np.std(parsed_calendars[i]['price_USD'])
    return listings_var
import seaborn as sns
sns.set(color_codes=True)
# print parsed_calendars
# print find_orphans(parsed_calendars, 3)
listing_var = listings_price_var(calendars)
# plt.hist(listing_var.values())
sns.distplot(listing_var.values(), bins=20, kde=False, rug=True);
sns.axlabel('Price Std', 'Number of Listings')
sns.plt.title('Listing Price Variance Distribution')<jupyter_output><empty_output><jupyter_text>Find the distribution of orphan's day<jupyter_code>Orphans_dict = defaultdict(int)
Orphans_dict['b'] += 1
Orphans_dict.keys()
num_1d = 0
num_2d = 0
num_3d = 0

Orphans_dict = defaultdict(int)
for i in parsed_calendars.keys():
    one = find_orphans(parsed_calendars[i], 1)
    two = find_orphans(parsed_calendars[i], 2)
    three = find_orphans(parsed_calendars[i], 3)
    
    num_1d += len(one)
    num_2d += len(two)
    num_3d += len(three)
    
    for i in one['Start']:
        Orphans_dict[i] += 1
    for i in two['Start']:
        Orphans_dict[i] += 1
    for i in three['Start']:
        Orphans_dict[i] += 1
        
print num_1d, num_2d, num_3d
import collections
o_Orphans_dict = collections.OrderedDict(sorted(Orphans_dict.items()))
o_Orphans_dict.keys()
sns.barplot(o_Orphans_dict.keys()[:30], o_Orphans_dict.values()[:30])
sns.plt.title('Number of Orphan\'s day Distributed on Timeline' )
sns.axlabel("Start Date","Number of Orphan's Day")
plt.xticks(rotation=75)

y = [24, 5, 4, 11, 7, 6, 5, 8, 8, 12, 4, 5, 5, 10, 9, 9, 8, 8, 11, 6, 7, 5, 6, 6, 5, 9, 5, 11, 6, 39]
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]
sns.barplot(x, y)
sns.plt.title('Finding Professional Hosts' )
sns.axlabel("Number of Booked Days in One Month","Number of Hosts")<jupyter_output><empty_output>
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