Beispiel #1
0
 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']))
Beispiel #2
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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)
Beispiel #3
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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))