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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 cumulative sum of each group in Pandas DataFrame

schedule Aug 12, 2023
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Python●Pandas
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Consider the following DataFrame:

import pandas as pd
df = pd.DataFrame({'name':['alex','alex','bob','bob','cathy'],
'score':[3,4,5,6,9]})
df
name score
0 alex 3
1 alex 4
2 bob 5
3 bob 6
4 cathy 9

To group by name, and then compute the cumulative sum for each group:

s = df.groupby('name')['score'].cumsum() # returns a Series
s
0 3
1 7
2 5
3 11
4 9
Name: score, dtype: int64

To add this Series as a new column to the original DataFrame:

df['cum_score'] = s
df
name score cum_score
0 alex 3 3
1 alex 4 7
2 bob 5 5
3 bob 6 11
4 cathy 9 9
robocat
Published by Isshin Inada
Edited by 0 others
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