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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Getting multiple aggregates of a column after grouping in Pandas
schedule Aug 12, 2023
Last updated local_offer
Tags Python●Pandas
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To get multiple aggregates of a column after grouping, call agg(~) method using either:
a list of aggregate functions
keyword arguments, where the keyword becomes the resulting column label
Example
Consider the following DataFrame:
df = pd.DataFrame({"price":[200,300,700, 900], "brand":["apple", "google", "apple", "google"], "device":["phone","phone","computer","phone"]})df
price brand device0 200 apple phone1 300 google phone2 700 apple computer3 900 google phone
List of functions
To compute multiple aggregates, pass in a list like so:
df.groupby("brand").agg(["mean",np.max])
price mean amaxbrand apple 450 700google 600 900
Keyword arguments
To compute multiple aggregates using keyword arguments:
df.groupby("brand")["price"].agg(mean_price="mean", max_price=np.max)
mean_price max_pricebrand apple 450 700google 600 900
Note the following:
you must specify which column to aggregate (
"price"in this case).the keywords (e.g.
mean_price) has become the resulting column label
Published by Isshin Inada
Edited by 0 others
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