table_a = pd.DataFrame(data=[
[
children_mean, adults_32_mean, adults_48_mean, adults_66_mean,
adults_plus_mean
],
[
childrenRev_mean, youngRev_32_mean, adultsRev_48_mean,
adultsRev_66_mean, adultsRev_plus_mean
],
[
children_rating, adults_32_rating, adults_48_rating, adults_66_rating,
adults_plus_rating
],
[
children_nights, adults_32_nights, adults_48_nights, adults_66_nights,
adults_plus_nights
], [children_wd, adults_32_wd, adults_48_wd, adults_66_wd, adults_plus_wd],
[children_we, adults_32_we, adults_48_we, adults_66_we, adults_plus_we],
[
children_nation_rev, adults_32_nation_rev, adults_48_nation_rev,
adults_66_nation_rev, adults_plus_nation_rev
],
[
children_nation_rating, adults_32_nation_rating,
adults_48_nation_rating, adults_66_nation_rating,
adults_plus_nation_rating
],
[
children_nation_upselling, adults_32_nation_upselling,
adults_48_nation_upselling, adults_66_nation_upselling,
adults_plus_nation_upselling
]
],
index=index_age,
columns=columns_age)
table_m_s = pd.DataFrame(data=[
[
Solo_Travellers_mean, Family_mean, Groups_mean, Couples_mean,
Business_mean
],
[
Solo_TravellersRev_mean, FamilyRev_mean, GroupsRev_mean,
CouplesRev_mean, BusinessRev_mean
],
[
Solo_Travellers_rating, Family_rating, Groups_rating, Couples_rating,
Business_rating
],
[
Solo_Travellers_nights, Family_nights, Groups_nights, Couples_nights,
Business_nights
], [Solo_Travellers_wd, Family_wd, Groups_wd, Couples_wd, Business_wd],
[Solo_Travellers_we, Family_we, Groups_we, Couples_we, Business_we],
[
Solo_Travellers_nation_rev, Family_nation_rev, Groups_nation_rev,
Couples_nation_rev, Business_nation_rev
],
[
Solo_Travellers_nation_rating, Family_nation_rating,
Groups_nation_rating, Couples_nation_rating, Business_nation_rating
],
[
Solo_Travellers_nation_upselling, Family_nation_upselling,
Groups_nation_upselling, Couples_nation_upselling,
Business_nation_upselling
]
],
index=index_mss,
columns=columns_mss)
import plotly.graph_objs as go
from settings import hotel_data, pd
""" CHOROPLETH VALUES """
values_choro = pd.DataFrame(
hotel_data.groupby('Country')[['ADR Adjusted', 'Customer Satisfaction Rating']].mean()).reset_index().copy()
values_choro_count = pd.DataFrame(
hotel_data.groupby('Country')[['ADR Adjusted']].count()).reset_index().copy()
values_choro_sum = pd.DataFrame(
hotel_data.groupby('Country')[['ADR Adjusted']].sum()).reset_index().copy()
values_choro['N° Clients (count)'] = values_choro_count['ADR Adjusted']
values_choro['ADR Adjusted (total sum)'] = values_choro_sum['ADR Adjusted']
values_choro.rename(columns={'ADR Adjusted': 'ADR Adjusted (mean)', 'Customer Satisfaction Rating': 'Customer Satisfaction Rating (mean)'}, inplace=True)
data_map_revenues = [go.Choropleth(
locations=values_choro['Country'],
z=values_choro['ADR Adjusted (mean)'],
locationmode="country names",
showscale=False,
colorscale=[
[0, "rgb(5, 10, 172)"],
[0.35, "rgb(40, 60, 190)"],
[0.5, "rgb(70, 100, 245)"],
[0.6, "rgb(90, 120, 245)"],
[0.7, "rgb(106, 137, 247)"],
[1, "rgb(220, 220, 220)"]
],
'Upselling mean', 'Revenues mean', 'Rating mean',
'Length staying mean (days)', ' - N° Weekdays', ' - N° Weekend days',
'Nationality highest revenues', 'Nationality highest rating',
'Nationality highest upselling'
]
table_dc = pd.DataFrame(data=[
[TATO_mean, direct_mean, corporate_mean, gds_mean],
[TATORev_mean, directRev_mean, corporateRev_mean, gdsRev_mean],
[TATO_rating, direct_rating, corporate_rating, gds_rating],
[TATO_nights, direct_nights, corporate_nights, gds_nights],
[TATO_wd, direct_wd, corporate_wd, gds_wd],
[TATO_we, direct_we, corporate_we, gds_we],
[TATO_nation_rev, direct_nation_rev, corporate_nation_rev, gds_nation_rev],
[
TATO_nation_rating, direct_nation_rating, corporate_nation_rating,
gds_nation_rating
],
[
TATO_nation_upselling, direct_nation_upselling,
corporate_nation_upselling, gds_nation_upselling
]
],
index=index_dc,
columns=columns_dc)
table_dchannel = table_dc.reset_index()
table_dchannel1 = go.Table(
domain=dict(x=[0, 1], y=[0, 0.80]),
columnwidth=[2, 1, 1, 1],
columns_ct = ['Transient', 'Transient-Party', 'Contract', 'Group']
index_ct = index_age
table_ct = pd.DataFrame(data=[
[transient_mean, transient_party_mean, contract_mean, group_mean],
[
transientRev_mean, transient_partyRev_mean, contractRev_mean,
groupRev_mean
],
[transient_rating, transient_party_rating, contract_rating, group_rating],
[transient_nights, transient_party_nights, contract_nights, group_nights],
[transient_wd, transient_party_wd, contract_wd, group_wd],
[transient_we, transient_party_we, contract_we, group_we],
[
transient_nation_rev, transient_party_nation_rev, contract_nation_rev,
group_nation_rev
],
[
transient_nation_rating, transient_party_nation_rating,
contract_nation_rating, group_nation_rating
],
[
transient_nation_upselling, transient_party_nation_upselling,
contract_nation_upselling, group_nation_upselling
]
],
index=index_ct,
columns=columns_ct)
table_customer_type = table_ct.reset_index()