Below call: >>> gr = df.groupby([‘EVENT_ID’, ‘SELECTION_ID’], as_index=False) >>> res = gr.agg({‘ODDS’:[np.min, np.max]}) >>> res EVENT_ID SELECTION_ID ODDS amin amax 0 100429300 5297529 18 25 1 100429300 5297559 30 38 returns a frame with mulit-index columns. If you do not want columns to be multi-index either you may do: >>> res.columns = list(map(”.join, res.columns.values)) >>> … Read more
Just cast it as a BINARY(16). SELECT category, MIN(CAST(guid AS BINARY(16))) FROM myTable GROUP BY category You can cast it back later if necessary. WITH CategoryValue AS ( SELECT category, MIN(CAST(guid AS BINARY(16))) FROM myTable GROUP BY category ) SELECT category, CAST(guid AS UNIQUEIDENTIFIER) FROM CategoryValue
Which version of .NET? In 4.0 you can use: string.Join(“,”, integerArray); In 3.5 I would be tempted to just use: string.Join(“,”, Array.ConvertAll(integerArray, i => i.ToString())); assuming it is an array. Otherwise, either make it an array, or use StringBuilder.
These aggregates are not allowed because they cannot be recomputed solely based on the changed values. Some aggregates, like COUNT_BIG() or SUM(), can be recomputed just by looking at the data that changed. These are allowed within an indexed view because, if an underlying value changes, the impact of that change can be directly calculated. … Read more
I wasn’t fully convinced from the accepted answer, and JohnKnight’s answer helped, so here’s my point of view: First, let’s explain aggregate() in my own words: Prototype: aggregate(zeroValue, seqOp, combOp) Description: aggregate() lets you take an RDD and generate a single value that is of a different type than what was stored in the original … Read more
For MSSQL you can use this. It can be adopted for other platforms: it’s just maths and aggregates on logarithms. SELECT GrpID, CASE WHEN MinVal = 0 THEN 0 WHEN Neg % 2 = 1 THEN -1 * EXP(ABSMult) ELSE EXP(ABSMult) END FROM ( SELECT GrpID, –log of +ve row values SUM(LOG(ABS(NULLIF(Value, 0)))) AS ABSMult, … Read more
change temp[‘total’] = pd.DataFrame(project_data.groupby(col1)[col2].agg({‘total’:’count’})).reset_index()[‘total’] temp[‘Avg’] = pd.DataFrame(project_data.groupby(col1)[col2].agg({‘Avg’:’mean’})).reset_index()[‘Avg’] to temp[‘total’] = pd.DataFrame(project_data.groupby(col1)[col2].agg(total=”count”)).reset_index()[‘total’] temp[‘Avg’] = pd.DataFrame(project_data.groupby(col1)[col2].agg(Avg=’mean’)).reset_index()[‘Avg’] reason: in new pandas version named aggregation is the recommended replacement for the deprecated “dict-of-dicts” approach to naming the output of column-specific aggregations (Deprecate groupby.agg() with a dictionary when renaming). source: https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.25.0.html