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Getting multiple aggregates of a column after grouping in Pandas

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Data Aggregation Cookbook
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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tocTable of Contents
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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 device
0 200 apple phone
1 300 google phone
2 700 apple computer
3 900 google phone

List of functions

To compute multiple aggregates, pass in a list like so:

df.groupby("brand").agg(["mean",np.max])
price
mean amax
brand
apple 450 700
google 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_price
brand
apple 450 700
google 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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Published by Isshin Inada
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