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How to do pivot_table in dask with aggfunc 'min'?


How to do pivot_table in dask with aggfunc 'min'?

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How to do pivot_table in dask with aggfunc 'min'?
Tag : python , By : al.
Date : January 12 2021, 09:11 PM

it should still fix some issue I am trying to create a pivot table in either pandas or dask, but ofcourse I get a memory error in pandas. That's why I want to use dask, because I want to work with even larger files possibly. , It looks like Dask dataframe raises the following error
ValueError("aggfunc must be either 'mean', 'sum' or 'count'")

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Pandas Pivot_Table defined function aggfunc


Tag : python , By : Trevor Cortez
Date : March 29 2020, 07:55 AM
Does that help I am trying to apply a custom aggregation function to a pivot table, but keep receiving KeyError: 'PayoffUPB'. Is this a syntax problem with aggfunc, or do I need to use a lambda function here? Thank you for the help. , We can using groupby with unstack
df.groupby(['Month','Program']).apply(CPR).unstack()
Out[310]: 
Program          a          b          c
Month                                   
201801   45.963991  45.963991  21.528328
201802   27.928082  29.379551  35.364845
201803   55.358443  28.989114  21.528328

Difference between dask pivot_table and pandas pivot_table python


Tag : python , By : mhedberg
Date : March 29 2020, 07:55 AM
this one helps. Definitely Dask. The way pandas work is, it processes everything as a monolithic block in memory and is not parallelizable, while Dask is made to break the data frame into chunks that can be processed in parallel.

Pandas pivot_table replaces nan with 0 aggfunc='sum'


Tag : python , By : boney M
Date : March 29 2020, 07:55 AM
Does that help Alternative solution is use GroupBy.sum with parameter min_count=1, but there are removed non numeric columns:
df = (df.groupby(['indices', 'column'])
                ['start_value','end_value','delta','name','unit']
                 .sum(min_count=1) 
                  .unstack()
                    )
print (df)
        start_value         end_value          delta        
column       '1nan' 'other'    '1nan' 'other' '1nan' 'other'
indices                                                     
A               NaN     NaN    1000.0     NaN    NaN     NaN
df = df.pivot_table(index=['indices'], 
                    columns=['column'], 
                    values=['start_value','end_value','delta','name','unit'], 
                    aggfunc=lambda x: x.sum(min_count=1)
                    )
print (df)
        end_value    name            unit        
column     '1nan'  '1nan'  'other' '1nan' 'other'
indices                                          
A          1000.0  'test'  'test2'  'USD'   'USD'

pandas pivot_table multiple aggfunc


Tag : python , By : T11M
Date : March 29 2020, 07:55 AM

pandas pivot_table apply aggfunc last instance


Tag : python , By : Jason Vance
Date : March 29 2020, 07:55 AM
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