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Maximum value


Rank refers to the relative position, value, worth, complexity, power, level etc



Every time a rank finds a larger number than that stored have it send its new max to the root rank

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Synchronazing data between all MPI tasks

While i think gbn s answer is probably sufficient i m wondering whether use of an over clause to establish a max date per id attribute with which to reduce the select in a where clause wouldn t be faster than a rank

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What is the fastest way to query from a historical EAV database

If your rank is less than 4 it takes 0 instead it s the max part

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User centered top list

Intermediary dataframe the intermediary dataframe is col1 col2 col3 group feature g1 f1 12 9.500000 1000 f2 0 8.000000 200 g2 f1 2 7.000000 330 f2 3 7.000000 331 f3 1 7.000000 100 g3 f1 7 7.666667 101 now for the final dataframe which finally gives group feature col1 col2 col3 0 g1 f1 1.0 1.0 1.0 1 g1 f2 2.0 2.0 2.0 2 g2 f1 2.0 1.0 2.0 3 g2 f2 1.0 1.0 1.0 4 g2 f3 3.0 1.0 3.0 5 g3 f1 1.0 1.0 1.0 for the second part of the question i would just change the indexing of the intermediary dataframe and compute rank after grouping on feature which gives feature group col1 col2 col3 0 f1 g1 1.0 1.0 1.0 1 f1 g2 3.0 3.0 2.0 2 f1 g3 2.0 2.0 3.0 3 f2 g1 2.0 1.0 2.0 4 f2 g2 1.0 2.0 1.0 5 f3 g2 1.0 1.0 1.0;i would use groupby on to produce an intermediary dataframe containing the sum avg and max columns not the rank and then again groupby on group only to produce the rank

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Pandas Add new "rank" columns for every column

You might replace the max subquery with a rank max is usually slower only when cus_id is the pi rank might be worse

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Large Table vs Multiple Tables - Normalized Data

If no rank is lower than no rank number there are no gaps in the table the query returns the max number + 1

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Returning the lowest integer not in a list in SQL

So i want to generate rank for second set of values but starting with value greater than max from first set

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Pig Latin - adding values from different bags?

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