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**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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# Calculating percentiles of a DataFrame in Pandas

*schedule*Mar 5, 2023

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Tags Python●Pandas

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Check out the

**interactive map of data science**To calculate percentiles in Pandas, use the `quantile(~)`

method.

# Examples

Consider the following DataFrame:

```
df
A B0 2 51 4 62 6 73 8 8
```

## Column-wise

To compute the 25th percentile of each column:

```
df.quantile(0.25)
A 3.50B 5.75Name: 0.25, dtype: float64
```

By default the `quantile(~)`

method computes percentiles column-wise.

## Row-wise

To compute the 50th percentile of each row:

```
df.quantile(q=0.5, axis=1)
0 3.51 5.02 6.53 8.0Name: 0.5, dtype: float64
```

By specifying `axis=1`

we compute the 50th percentile by row.

## Multiple percentiles

To get the values at the 50th and 75th percentiles for each column:

```
df.quantile([0.5, 0.75]) # returns a DataFrame
A B0.50 5.0 6.500.75 6.5 7.25
```

Published by Arthur Yanagisawa

Edited by 0 others

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