def show_summary(self, vehicle):
'''
Print summary about chosen vehicle
'''
summary = FN.get_summary(vehicle)
print('\nSummary for {0}:'.format(vehicle['reg_no']))
print('Fuel:')
print('Mpg Min {:.2f}, Avg {:.2f}, Max {:.2f}'.format(summary['mpg']['min'], summary['mpg']['avg'], summary['mpg']['max']))
print('Trip Min {:.1f}, Avg {:.1f}, Max {:.1f}'.format(summary['trip']['min'], summary['trip']['avg'], summary['trip']['max']))
print('PPL Min {:.3f}, Avg {:.3f}, Max {:.3f}'.format(summary['ppl']['min'], summary['ppl']['avg'], summary['ppl']['max']))
print('Cost Min {:.2f}, Avg {:.2f}, Max {:.2f}'.format(summary['cost']['min'], summary['cost']['avg'], summary['cost']['max']))
print('Totals:')
print('Total miles:\t\t {:.1f}'.format(summary['trip']['total']))
print('Total Fuel cost:\t {:.2f}'.format(summary['cost']['total']))
print('Total Service cost:\t {:.2f}'.format(summary['service_cost']))
print('Running cost:\t\t {:.2f}'.format(summary['running_cost']))
print('Total cost:\t\t {:.2f}'.format(summary['total_cost']))
print('Running cost/mile:\t {:.2f}'.format(summary['rcpm']))
print('Total cost/mile:\t {:.2f}'.format(summary['tcpm']))
def app():
df_paths = {
'Sales': 'data/clean/sales/sales_1522.csv',
'Revenue': 'data/clean/revenue/clean_revenue.csv'
}
start = st.sidebar.date_input('Start:', dt.date(2021, 1, 1))
end = st.sidebar.date_input('End:', dt.date.today() - timedelta(1))
choice = st.selectbox('Choose Data:', list(df_paths.keys()))
df = get_clean_data(df_paths[choice], low_memory=False)
csv_goals = pd.read_csv('data/clean/revenue/revenue_goals.csv')
df_goals = pd.DataFrame(csv_goals).set_index('Month')
if choice == 'Sales':
numeric_cols = ['TotalPrice', 'ProgramSize']
elif choice == 'Revenue':
numeric_cols = ['GrossSalesAmount', 'Size']
pre_select_option = st.selectbox('Program quick select', [
'None', 'All', 'Tick & Mosquito', 'Turf Care + Addons', 'Snow',
'Miscellaneous'
])
pre_selects = {
'Tick & Mosquito': TM_list,
'Turf Care + Addons': OTC_with_addons,
'Snow': Snow,
'Miscellaneous': Miscellaneous,
'All': Programs
}
if 'FZ' in OTC_with_addons.keys():
del OTC_with_addons['FZ']
if pre_select_option in pre_selects.keys():
insert = pre_selects[pre_select_option]
programs = st.multiselect('Choose Programs to Include:',
list(Programs.keys()),
default=list(insert.keys()))
else:
programs = st.multiselect('Choose Programs to Include:',
list(Programs.keys()))
column_of_interest = st.selectbox('Choose Stat:', numeric_cols)
branch_list = [x
for x in list(df['Branch'].unique())] #if str(x) != 'nan']
state_list = [
x for x in list(df['State'].unique())
] #if str(x) != 'nan' and len(str(x)) > 1 and str(x).isalpha()]
branches = st.multiselect('Choose branches to include:',
sorted(branch_list),
default=sorted(branch_list))
states = st.multiselect('Choose states to include:',
sorted(state_list),
default=sorted(state_list))
try:
get_summary(df=df,
column_of_interest=column_of_interest,
start=start,
end=end,
choice=choice,
programs=programs,
branches=branches,
states=states)
except IndexError:
st.write('No program selected / No sales for given criteria.')
df_graph = get_year_end_results(df,
2016,
2022,
start_date_string=start.strftime('%m/%d'),
end_date_string=end.strftime('%m/%d'))
df_diff = df_graph.diff(periods=1)
#df_graph.loc['Average', :] = df_graph.agg(np.nanmean, axis=0)
fig = make_subplots(rows=1,
cols=2,
subplot_titles=(f'{choice} Amount',
'Percentage Growth'))
fig.add_trace(go.Bar(x=df_diff.index[1:],
y=df_diff['T&M'][1:],
name='T&M',
legendgroup='T&M',
marker_color='#FFA15A'),
row=1,
col=1)
fig.add_trace(go.Bar(x=df_diff.index[1:],
y=df_diff['OTC'][1:],
name='OTC',
legendgroup='OTC',
marker_color='#00CC96'),
row=1,
col=1)
fig.add_trace(go.Bar(x=df_diff.index[1:],
y=df_diff['Total'][1:],
name='Total',
legendgroup='Total',
marker_color='#AB63FA'),
row=1,
col=1)
fig.add_trace(go.Bar(x=df_graph.index[1:],
y=df_graph['T&M Growth'][1:],
name='T&M',
legendgroup='T&M',
showlegend=False,
marker_color='#FFA15A'),
row=1,
col=2)
fig.add_trace(go.Bar(x=df_graph.index[1:],
y=df_graph['OTC Growth'][1:],
name='OTC',
legendgroup='OTC',
showlegend=False,
marker_color='#00CC96'),
row=1,
col=2)
fig.add_trace(go.Bar(x=df_graph.index[1:],
y=df_graph['Total Growth'][1:],
name='Total',
legendgroup='Total',
showlegend=False,
marker_color='#AB63FA'),
row=1,
col=2)
fig.update_layout(
title=
f'''YoY {choice} Growth by Program ({start.strftime('%m/%d')} - {end.strftime('%m/%d')})''',
barmode='group')
df_graph_format = df_graph
df_graph_format['T&M'] = df_graph_format['T&M'].map('${:,.2f}'.format)
df_graph_format['OTC'] = df_graph_format['OTC'].map('${:,.2f}'.format)
df_graph_format['Total'] = df_graph_format['Total'].map('${:,.2f}'.format)
df_graph_format['T&M Growth'] = df_graph_format.apply(
lambda row: format_percentages(row, 'T&M Growth'), axis=1)
df_graph_format['OTC Growth'] = df_graph_format.apply(
lambda row: format_percentages(row, 'OTC Growth'), axis=1)
df_graph_format['Total Growth'] = df_graph_format.apply(
lambda row: format_percentages(row, 'Total Growth'), axis=1)
st.markdown(
f'''**Yearly {choice} Amount + Growth by Program ({start.strftime('%m/%d')} - {end.strftime('%m/%d')})**'''
)
st.dataframe(df_graph_format)
st.plotly_chart(fig)
### SERVICES PER MONTH ###
st.markdown(f'''**Average Number of Services Per Month**''')
csv_services_perMonth = pd.read_csv(
'data/clean/revenue/average_services_per_month.csv')
df_services_perMonth = pd.DataFrame(csv_services_perMonth)
[
df_services_perMonth.drop(columns=[i], inplace=True)
for i in df_services_perMonth.columns if 'Unnamed' in i
]
program_choice = st.selectbox(
'Choose Program:',
sorted(list(df_services_perMonth['ProgramCode'].unique())))
stat_choice = st.selectbox('Choose Summary Statistic: ',
list(stat_conversions.keys()))
df_services_filter = df_services_perMonth.loc[
df_services_perMonth['ProgramCode'] == program_choice]
df_services_filter = df_services_filter.drop(columns=['DoneDateMonthYear'])\
.groupby(by=['Month']).agg(stat_conversions[stat_choice][0])\
.reindex(months)
fig2 = go.Figure(data=[
go.Bar(
x=df_services_filter.index,
y=df_services_filter['NumServices'],
)
])
fig2.update_layout(
title=
f'{program_choice} {stat_conversions[stat_choice][1]} Number of Services per Month (2016 - 2021)',
xaxis_title='Month',
yaxis_title='Number of Services')
st.plotly_chart(fig2)
### REVENUE GOALS ###
st.markdown(f'''**Monthly Revenue Goals**''')
st.dataframe(df_goals)
def summary_detail(request, year, month, day, trial):
data ={}
data["current_date"]= functions.create_current_date(year,month,day,trial)
data["date_list"]= functions.date_list()
functions.get_summary(data)
return render(request,'summary.html',data, context_instance=RequestContext(request))
