Pandas
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Applying a function to multiple columns in groupsCalculating percentiles of a DataFrameCalculating the percentage of each value in each groupComputing descriptive statistics of each groupDifference between a group's count and sizeDifference between methods apply and transform for groupbyGetting cumulative sum of each groupGetting descriptive statistics of DataFrameGetting multiple aggregates of a column after groupingGetting n rows with smallest column value in each groupGetting number of distinct rows in each groupGetting size of each groupGetting specific group after groupbyGetting the first row of each groupGetting the last row of each groupGetting the top n rows with largest column value in each groupGetting unique values of each groupGrouping by multiple columnsGrouping without turning group column into indexMerging rows within a group togetherNaming columns after aggregationSorting values within groups
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Sorting values within groups in Pandas
schedule Aug 10, 2023
Last updated local_offer
Tags Python●Pandas
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Grouping by single column
Consider the following DataFrame:
df
A B group0 6 3 a1 3 4 b2 5 8 a3 4 2 b
Using sort_values method
To get the row with the smallest value for A in each group:
A B group1 3 4 b2 5 8 a
If you want the largest value instead, set ascending=False for sort_values(~).
Explanation
Here, we are first sorting df by column A using sort_values(~):
A B group1 3 4 b3 4 2 b2 5 8 a0 6 3 a
We then group by column group, and fetch the first row of each group using head(1) (you could also use first()). This is guaranteed to work because groupby(~) preserves the ordering of the rows, that is, the order of the rows computed by sort_values(~) will be respected.
Published by Isshin Inada
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