How to use the .agg () and .apply () methods in the groupby () method

This article mainly shows you how to use the .agg () and .apply () methods in the groupby () method. The content is easy to understand and clear. I hope it can help you solve your doubts. Let the editor lead you to study and learn how to use the .agg () and .apply () methods in the groupby () method.

After .groupby (), you can follow the .agg () and .apply () parameters. The functions of these two parameters are magical, but the usage is a little different.

1. Construct a DataFrame first

Import pandas as pddf = pd.DataFrame ({"year": ["2020", "2019", "2020", "2020", "2019", "2019"], "commodity": ["apple", "watermelon", "litchi", "longan", "pineapple", "pineapple"], "sales": [100200300500600]}) df

two。 Observe the results made by .agg ()

Df.groupby (["year"]) .agg (lambda x: print (x))

3. The result made with .apply ()

Df.groupby (["year"]) .apply (lambda x: print (x))

It is not difficult to see that. Apply () handles objects such as DataFrame data tables, while .agg () passes in only one column at a time.

4. Take a look at other uses of .agg ().

Df.groupby (["year"]) .agg (["sum", "mean", "max", "min"])

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