WebJan 1, 2016 · Using reshape is quicker than calling groupby/cumcount and pivot, but it is less robust since it relies on the values in y appearing in the right order. Share Improve this answer WebOct 3, 2024 · Yes, you can do sort_values ( [col1,col2,col3,col4...]) and pass ascending = [True, False,...] with the same length as the list of columns. First we use your logic to create the % column, but we multiply by 100 …

Python/Pandas: How to combine cumsum and cumcount with agg function?

WebJun 5, 2024 · df ["AddCol"] = df.groupby ("Vela").ngroup ().diff ().ne (0).cumsum () where we first get the group number each distinct Vela belongs to (kind of factorize) then take the first differences and see if they are not equal to 0. This will sort of give the "turning" points from one group to another. Then we cumulatively sum them, to get WebMay 21, 2014 · you modifying values when iterating that is a no no in python (it can work as iter rows will in a single dtype case return a view), but in general a bad idea); always return a new frame (or copy and modify the copy) – Jeff May 21, 2014 at 19:26 use pd.to_datetime () to convert your dates all in one shot – Jeff May 21, 2014 at 19:29 how does flashpay work

python - Pivot a groupby object in Pandas? - Stack Overflow

WebAug 3, 2016 · You can use cumcount with pivot_table, where parameter index use columns userid and dt, so it looks like create df2 is not necessary:. df['cols'] = 'name_' + (df.groupby(['userid','dt']).cumcount() + 1).astype(str) print (df.pivot_table(index=['userid', 'dt'],columns='cols', values='name', aggfunc=''.join)) cols name_1 name_2 userid dt 123 … WebDec 1, 2016 · There is a special function for such operation: cumcount >>> df = pd.DataFrame ( [ ['a'], ['a'], ['a'], ['b'], ['b'], ['a']], columns= ['A']) >>> df A 0 a 1 a 2 a 3 b 4 … photo fond site web