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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Getting the first row of each group in Pandas
schedule Aug 11, 2023
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Tags Python●Pandas
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To get the first row of each group, call first() after grouping.
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
To get the first row of each brand group:
df.groupby("brand").first()
price devicebrand apple 200 phonegoogle 300 phone
Note that groupby(~) method preserves the ordering of the rows, and so it is guaranteed to get the first occurrence of each brand in this case.
* * *
As a side note, you could also fetch the first n rows of each group using the head(~) method:
df.groupby("brand").head(2)
price brand device0 200 apple phone1 300 google phone2 700 apple computer3 900 google phone
Published by Isshin Inada
Edited by 0 others
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