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Django and chart.js - one bar per 'date' in model

I have a model that contains a date and a number. There are multiple inputs from the same date that I want to merge into 1 bar on the chart. How can I show each date only once but add up all of the totalPacks under that date?
model:
Views:
def homepage(request):
labels = []
data = []
queryset = DailyCountsModel.objects.order_by('-date')
for jobs in queryset:
labels.append(jobs.date)
data.append(jobs.totalPacks)
return render(request,'index.html', {
'labels': labels,
'data': data,
})
Currently this chart will show one bar per entry.. I can't think of how I could do this. Any ideas? I'm guessing somehow I would need to check to see how many items they are with the 'date' of 2021-08-23 and add up the 'totalPacks', I'm just not sure how I would do this

from django.db.models import Count
result = (DailyCountsModel.objects
.values('totalPacks', 'date')
.annotate(dcount=Count('totalPacks'))
.order_by('-date')
)

how to extract only day from timestamp in Django

I want to get a specific date like "8" out of (2021-8-3) but it's showing like this image
how can I extract the specific date?
usertime = User.objects.filter(groups__name = 'patient').values('date_joined').annotate(date_only=Cast('date_joined', DateField()))

from django.db.models import F, Func,Value, CharField
usertime = (User.objects.filter(groups__name = 'patient').values('date_joined')
.annotate(date_only=Func(
F('date_joined'),
Value('MM'),
function='to_char',
output_field=CharField()
)
).values('date_only'))
Try this,
got a reference from #Yannics answer at: https://stackoverflow.com/a/60924664/5804947
you can further use YYYY / DD for years/date respectively under the Value field and works fine when the PostgreSQL database is used.
ANOTHER METHOD
from django.db.models.functions import Extract
usertime = User.objects.filter(groups__name = 'patient').values('date_joined').annotate(date_only=Extract('date_joined', 'month'))

Cannot filter my Django model depence on created_date

I am trying to do a chart. My database has created_date. I am getting product data every day about 150 times and I want to see a daily increase and decrease of my data. I have no problem with my front end and Django-template (I try manual data and it works well) I just want to see the last 7 days chart.
When I use Products.objects.filter(created_dates=days) filter method I am getting empty Queryset.
I already try created_dates__gte=startdate,created_dates__lte=enddate it return empty Queryset to.
I also try created_dates__range to there is no answer too.
I just get data from created_dates__gte=days but I don't want these data.
view.py
from datetime import date,timedelta
import datetime
def data_chart(request):
data = []
week_days = [datetime.datetime.now().date()-timedelta(days=i) for i in range (1,7)]
for days in week_days:
product_num = Products.objects.filter(created_dates=days)
date =days.strftime("%d.%m")
item = {"day": date,"value":len(product_num)}
data.append(item)
return render(request, 'chartpage.html', {'data': data})
In my database, I have thousands of data and my daily data about 150. My created_dates column format like this.
created_dates col:
2020-10-19 09:39:19.894184
So what is wrong with my code?. Could you please help?

You are trying to compare DateTimeField type (created_dates) with Date type (week_days is list of days) so maybe You should try __date lookup.
product_num = Products.objects.filter(created_dates__date=days)
https://docs.djangoproject.com/en/3.0/ref/models/querysets/#date
Furthermore maybe You should consider start using Count() database function with group by instead of iterating over days.
Here is great explanation:
https://stackoverflow.com/a/19102493/5160341

You should be able to do this with a single aggregation query:
import datetime
from django.db.models import Count
def data_chart(request):
cutoff = datetime.date.today() - datetime.timedelta(days=7)
raw_data = (
Products.objects.filter(created_dates__gte=cutoff)
.values_list("created_dates__date")
.annotate(count=Count("id"))
.values_list("created_dates__date", "count")
)
data = [{"day": str(date), "value": value} for (date, value) in raw_data]
return render(request, "chartpage.html", {"data": data})

Django, how to extract values list monthly from DateField?

I have the following models:
class Materiale(models.Model):
sottocategoria = models.ForeignKey(Sottocategoria, on_delete=models.CASCADE, null=True)
quantita=models.DecimalField(')
prezzo=models.DecimalField()
data=models.DateField(default="GG/MM/YYYY")
I wanna calculate the value given by the following expressions PREZZO*QUANTIA in a monthly's view (in other words the total sum of PRZZO*QUANTITA of all items in a single month), but my code does not work:
Monthly_views=Materiale.objects.filter(data__year='2020').values_list('month').annotate(totale_mensile=F(('quantita')*F('prezzo')))

Use values() method instead of values_list()
from django.db.models import F, Sum
result = Materiale.objects.annotate(totale_mensile=F('quantita') * F('prezzo')
).values('data__month').annotate(totale_mensile_sum=Sum('totale_mensile')))
or simply
result = Materiale.objects.values('data__month').annotate(totale_mensile_sum=Sum(F('quantita') * F('prezzo')))

Try filtering by month also
Monthly_views=Materiale.objects.filter(data__year='2020').filter(data_month='4')

Converting Django QuerySet to pandas DataFrame

I am going to convert a Django QuerySet to a pandas DataFrame as follows:
qs = SomeModel.objects.select_related().filter(date__year=2012)
q = qs.values('date', 'OtherField')
df = pd.DataFrame.from_records(q)
It works, but is there a more efficient way?

import pandas as pd
import datetime
from myapp.models import BlogPost
df = pd.DataFrame(list(BlogPost.objects.all().values()))
df = pd.DataFrame(list(BlogPost.objects.filter(date__gte=datetime.datetime(2012, 5, 1)).values()))
# limit which fields
df = pd.DataFrame(list(BlogPost.objects.all().values('author', 'date', 'slug')))
The above is how I do the same thing. The most useful addition is specifying which fields you are interested in. If it's only a subset of the available fields you are interested in, then this would give a performance boost I imagine.

Convert the queryset on values_list() will be more memory efficient than on values() directly. Since the method values() returns a queryset of list of dict (key:value pairs), values_list() only returns list of tuple (pure data). It will save about 50% memory, just need to set the column information when you call pd.DataFrame().
Method 1:
queryset = models.xxx.objects.values("A","B","C","D")
df = pd.DataFrame(list(queryset)) ## consumes much memory
#df = pd.DataFrame.from_records(queryset) ## works but no much change on memory usage
Method 2:
queryset = models.xxx.objects.values_list("A","B","C","D")
df = pd.DataFrame(list(queryset), columns=["A","B","C","D"]) ## this will save 50% memory
#df = pd.DataFrame.from_records(queryset, columns=["A","B","C","D"]) ##It does not work. Crashed with datatype is queryset not list.
I tested this on my project with >1 million rows data, the peak memory is reduced from 2G to 1G.

Django Pandas solves this rather neatly: https://github.com/chrisdev/django-pandas/
From the README:
class MyModel(models.Model):
full_name = models.CharField(max_length=25)
age = models.IntegerField()
department = models.CharField(max_length=3)
wage = models.FloatField()
from django_pandas.io import read_frame
qs = MyModel.objects.all()
df = read_frame(qs)

From the Django perspective (I'm not familiar with pandas) this is fine. My only concern is that if you have a very large number of records, you may run into memory problems. If this were the case, something along the lines of this memory efficient queryset iterator would be necessary. (The snippet as written might require some rewriting to allow for your smart use of .values()).

You maybe can use model_to_dict
import datetime
from django.forms import model_to_dict
pallobjs = [ model_to_dict(pallobj) for pallobj in PalletsManag.objects.filter(estado='APTO_PARA_VENTA')]
df = pd.DataFrame(pallobjs)
df.head()