Show statistics on Wagtail home page

trips/templates/trips/statistics.html

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+<section class="object collapsible">
+  <div class="title-wrapper">{{ heading }}</div>
+  <div class="object-layout">
+    <div class="object-layout_big-part" style="margin-top: 40px; padding-top: 1ex; padding-bottom: 1ex">
+      {{ table_html|safe }}
+    </div>
+  </div>
+</section>

trips/wagtail_hooks.py

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+import pandas as pd
+from dateutil.relativedelta import relativedelta
+from django.db.models import Count, F, Sum
+from django.db.models.functions import TruncDate, TruncMonth
+from django.template.loader import render_to_string
+from django.utils import timezone
+from django.utils.translation import gettext_lazy as _
+from wagtail.core import hooks
+
+from budget.models import DeviceDailyCarbonFootprint, Prize
+from trips.models import Leg
+
+
+class StatisticsPanel:
+    name = 'statistics'
+    order = 200
+
+    def __init__(self, request, heading, df):
+        self.request = request
+        self.heading = heading
+        self.df = df
+
+    def render(self):
+        return render_to_string('trips/statistics.html', {
+            'heading': self.heading,
+            'table_html': self.df.to_html(index_names=False),
+        }, request=self.request)
+
+
+@hooks.register('construct_homepage_panels')
+def add_statistics_panel(request, panels):
+    # Calculate number of active devices per day in the last 14 days
+    # two_weeks_ago = timezone.now() - relativedelta(days=14)
+    two_weeks_ago = timezone.now() - relativedelta(days=1000)
+    active_devices_per_day = (Leg.objects
+                              .values(start=TruncDate('start_time'))
+                              .filter(start__gte=two_weeks_ago)
+                              .annotate(count=Count('trip__device'))
+                              .order_by('start'))
+    active_devices_per_day = pd.DataFrame(
+        data=[{'day': item['start'].isoformat(), 'value': item['count']} for item in active_devices_per_day]
+    )
+    if not active_devices_per_day.empty:
+        active_devices_per_day = active_devices_per_day.set_index('day')
+        active_devices_per_day = active_devices_per_day.rename(columns={'value': str(_("Number of devices"))})
+        panels.append(StatisticsPanel(request, _("Active devices per day"), active_devices_per_day))
+
+    # Calculate number of devices in each prize level
+    one_year_ago = timezone.now() - relativedelta(years=1)
+    prizes = (Prize.objects
+              .filter(prize_month_start__gte=one_year_ago)
+              .annotate(date=F('prize_month_start'), prize=F('budget_level__name'))
+              .values('date', 'budget_level', 'prize')
+              .annotate(count=Count('device'))
+              .order_by('date', 'budget_level__carbon_footprint'))
+    prizes = pd.DataFrame(data=prizes)
+    if not prizes.empty:
+        prizes['date'] = pd.to_datetime(prizes['date'])
+        assert all(date.day == 1 for date in prizes['date'])
+        # Print months in natural language
+        prizes['date'] = prizes['date'].dt.strftime('%Y-%m')
+        prizes = prizes.set_index('date')
+        prizes = prizes.groupby(['date', 'prize'], sort=False)['count'].sum().unstack('prize')
+        panels.append(StatisticsPanel(request, _("Awarded prizes per month"), prizes))
+
+    # Calculate histograms of carbon footprints for certain months
+    monthly_footprints = (DeviceDailyCarbonFootprint.objects
+                          .annotate(month=TruncMonth('date'))
+                          .filter(month__gte=one_year_ago)
+                          .values('device', 'month')
+                          .annotate(monthly_footprint=Sum('carbon_footprint'))
+                          .order_by())
+    monthly_footprints = pd.DataFrame(data=monthly_footprints)
+    bins = [i for i in range(0, 275, 25)] + [float('inf')]
+    histograms = []
+    months = list(sorted(monthly_footprints.month.unique()))
+    for month in months:
+        histogram = list(monthly_footprints[monthly_footprints.month == month]
+                         .monthly_footprint
+                         .value_counts(bins=bins))
+        histograms.append(histogram)
+    # Treat last bin separately because "250-inf kg" is ugly
+    intervals = [f'{a}–{b} kg' for a, b in zip(bins[:-1], bins[1:-1])] + [f'≥ {bins[-2]} kg']
+    histograms_df = pd.DataFrame(data=histograms, index=months, columns=intervals)
+    if not histograms_df.empty:
+        histograms_df = histograms_df.reset_index()
+        histograms_df['index'] = pd.to_datetime(histograms_df['index'])
+        histograms_df['index'] = histograms_df['index'].dt.strftime('%Y-%m')
+        histograms_df = histograms_df.set_index('index')
+        panels.append(StatisticsPanel(request, _("Number of devices by carbon footprint"), histograms_df))