You can define a function which returns your different states “Full”, “Partial”, “Empty”, etc and then use df.apply to apply the function to each row. Note that you have to pass the keyword argument axis=1 to ensure that it applies the function to rows. import pandas as pd def alert(row): if row[‘used’] == 1.0: return … Read more
Update: As of version 3.31.0 (released on 2020-01-22), SQLite supports computed columns so the answer below applies to versions prior to 3.31.0 SQLite doesn’t supported computed columns. However your problem can be solved with a relatively simple SQL Query, you can then create a view to make it appear like a table with the extra … Read more
Indeed. Create a separate file, for instance, EmployeeExtension.cs. In this file, place the following code: public partial class Employee { public decimal Salary { get { return Hours * Wage; } } } LINQ to SQL and Entity Framework classes are generated with the partial keyword to allow you to split the definition over many … Read more
Using Derived Columns in a predicate You’ll need to wrap the inner query in a derived table or CTE in order to be able to use derived columns in the WHERE clause (Also, note SUM() is specified just once, using the results of the multiplication): SELECT x.Code, x.AccountNumber, x.Sales FROM ( SELECT p.Code, c.AccountNumber, SUM(p.UnitPrice … Read more
You just need to create it with schemabinding. SQL Server will then verify whether or not it meets the criteria to be considered as deterministic (which it does as it doesn’t access any external tables or use non deterministic functions such as getdate()). You can verify that it worked with SELECT OBJECTPROPERTY(OBJECT_ID(‘[dbo].[FullNameLastFirst]’), ‘IsDeterministic’) Adding the … Read more
You can do this easily manually for each column like this: df[‘A_perc’] = df[‘A’]/df[‘sum’] If you want to do this in one step for all columns, you can use the div method (http://pandas.pydata.org/pandas-docs/stable/basics.html#matching-broadcasting-behavior): ds.div(ds[‘sum’], axis=0) And if you want this in one step added to the same dataframe: >>> ds.join(ds.div(ds[‘sum’], axis=0), rsuffix=’_perc’) A B C … Read more
Use pandas.DataFrame.abs(). import pandas as pd df = pd.DataFrame(data={‘count’:[1, -1, 2, -2, 3, -3]}) df[‘count’] = df[‘count’].abs() print(df) count #0 1 #1 1 #2 2 #3 2 #4 3 #5 3
You just need to provide an alias for your aggregate columns in the CTE d as (SELECT duration, sum(totalitems) as sumtotalitems FROM [DrySoftBranch].[dbo].[mnthItemWiseTotalQty] (‘1’) AS BkdQty group by duration )
The problem you have here, to solve as elegantly as possible, uses very advanced SQLAlchemy techniques, so I know you’re a beginner, but this answer is going to show you all the way out to the end. However, solving a problem like this requires walking through one step at a time, and you can get … Read more