*chevron_left*Data 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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# Applying a function to multiple columns in groups in Pandas

Pandas

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Data Aggregation Cookbook

*schedule*Jul 1, 2022

local_offer Python●Pandas

Tags *toc*Table of Contents

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Consider the following DataFrame:

```
df = pd.DataFrame({"A":[2,3,4],"B":[5,6,7],"group":["a","a","b"]})df
A B group0 2 5 a1 3 6 a2 4 7 b
```

To group by `group`

, and then apply a function on multiple columns in each group:

```
def f(my_df): return pd.Series({"C": my_df["A"].sum() + my_df["B"].sum()})
```

df.groupby("group").apply(f)
Cgroup a 16b 11

Note the following:

our

`f`

function is called twice in this case - once for each groupargument for

`f`

is a DataFrame representing each group

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Published by Isshin Inada

Edited by 0 others

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