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# Getting multiple aggregates of a column after grouping in Pandas

schedule Aug 12, 2023
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local_offer
PythonPandas
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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

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