Fetching and Grouping Blood Pressure Records by Date

To fetch blood pressure records from the past 30 days and group them by date in your Django app, you can use Django's ORM with date filtering and annotation/aggregation features.

Here's a step-by-step implementation:

1. Filtering records from the past 30 days

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from django.utils import timezone
from datetime import timedelta
from django.db.models import Count

# Calculate the date 30 days ago
thirty_days_ago = timezone.now().date() - timedelta(days=30)

# Fetch records from the last 30 days
records = BloodPressure.objects.filter(
    date__gte=thirty_days_ago
).order_by('-date')

2. Grouping by date

There are a few approaches to group by date:

Option 1: Using values() and annotate()

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from django.db.models.functions import TruncDate

# Group by date and count records per date
grouped_records = BloodPressure.objects.filter(
    date__gte=thirty_days_ago
).annotate(
    day=TruncDate('date')  # Truncate datetime to date
).values('day').annotate(
    count=Count('id')
).order_by('-day')

# This gives you a queryset of dictionaries with 'day' and 'count' keys

Option 2: Group in Python (if you need the full record objects)

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from itertools import groupby
from operator import attrgetter

records = BloodPressure.objects.filter(
    date__gte=thirty_days_ago
).order_by('date')

# Group records by date
grouped_records = {}
for record in records:
    date_key = record.date.date()  # Convert datetime to date
    if date_key not in grouped_records:
        grouped_records[date_key] = []
    grouped_records[date_key].append(record)

3. Implementing in your view

PYTHONCopied
def blood_pressure_list(request):
    thirty_days_ago = timezone.now().date() - timedelta(days=30)
    
    # Get records grouped by date
    grouped_records = BloodPressure.objects.filter(
        date__gte=thirty_days_ago
    ).annotate(
        day=TruncDate('date')
    ).values('day').annotate(
        count=Count('id'),
        # You can add more aggregations here if needed
        # avg_systolic=Avg('systolic'),
        # avg_diastolic=Avg('diastolic')
    ).order_by('-day')
    
    return render(request, 'blood_pressure/list.html', {
        'grouped_records': grouped_records,
    })

4. Template example

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{% for group in grouped_records %}
    <h3>{{ group.day|date:"F j, Y" }}</h3>
    <p>Number of readings: {{ group.count }}</p>
    
    {% if group.avg_systolic %}
        <p>Average: {{ group.avg_systolic }}/{{ group.avg_diastolic }}</p>
    {% endif %}
    
    <!-- If you need to display individual records, you'll need to modify the view -->
{% endfor %}

The database approach (Option 1) is generally more efficient for large datasets as it pushes the grouping work to the database. The Python approach (Option 2) gives you more flexibility if you need to work with the full record objects.