Can you do a df.dtypes ? In the example below type is Int as it works fine. import pandas as pd ##group by 1 columns df = pd.DataFrame({‘ data’: [4610, 4611, 4612, 4613], ‘Category’: [2, 2, 7, 7]}) print df.groupby(‘Category’). mean() ##Mutiple columns to group by df1 = pd.DataFrame({‘ data’: [4610, 4611, 4612, 4613], ‘Category’: … Read more
There are various simpler and faster ways. 2x DISTINCT ON SELECT * FROM ( SELECT DISTINCT ON (name) name, week AS first_week, value AS first_val FROM tbl ORDER BY name, week ) f JOIN ( SELECT DISTINCT ON (name) name, week AS last_week, value AS last_val FROM tbl ORDER BY name, week DESC ) l … Read more
This will bring back totals per property and type SELECT PropertyID, TYPE, SUM(Amount) FROM yourTable GROUP BY PropertyID, TYPE This will bring back only active values SELECT PropertyID, TYPE, SUM(Amount) FROM yourTable WHERE EndDate IS NULL GROUP BY PropertyID, TYPE and this will bring back totals for properties SELECT PropertyID, SUM(Amount) FROM yourTable WHERE EndDate … Read more
I would focus on the example queries rather than your quote from the documentation. Aggregate calculates values for the entire queryset. Annotate calculates summary values for each item in the queryset. Aggregation >>> Book.objects.aggregate(average_price=Avg(‘price’)) {‘average_price’: 34.35} Returns a dictionary containing the average price of all books in the queryset. Annotation >>> q = Book.objects.annotate(num_authors=Count(‘authors’)) >>> … Read more
With data.table library(“data.table”) D <- fread( “ID Year Amount 3 2000 45 3 2000 55 3 2002 10 3 2002 10 3 2004 30 4 2000 25 4 2002 40 4 2002 15 4 2004 45 4 2004 50” ) D[, .(Amount=sum(Amount)), by=.(ID, Year)] and with base R: aggregate(Amount ~ ID + Year, data=D, FUN=sum) … Read more