Pandas
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chevron_leftData Aggregation Cookbook
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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Naming columns after aggregation in Pandas DataFrame
schedule Aug 12, 2023
Last updated local_offer
Tags Python●Pandas
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Consider the following DataFrame:
import pandas as pd 'name':['alex','bob','cathy','doge'],\ 'score':[3,4,5,6]})df
class name score0 a alex 31 a bob 42 b cathy 53 b doge 6
To name columns after aggregation:
Here, the named argument (e.g. my_mean_score) in agg(~) becomes the column label of the resulting DataFrame.
Published by Isshin Inada
Edited by 0 others
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