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I have a store in atoti, where I would like to create buckets based on a continuous variable.

Here is the screenshot to the store:

enter image description here

I am trying to create buckets based on age.

One solution I could think of is to, create a new column in the original data frame and then join this to the existing store.

is there is a smarter way to create a column based on another column on the fly without going back to the original data frame?

Raghav Vashisht
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1 Answers1

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Disclaimer: I am a data scientist at atoti. Well, you can use the read_pandas to read a new data frame and join it to the existing store on the fly.

Something like this should work.

# age group buckets
age_groups_store = session.read_pandas(
    pd.DataFrame(
        data=[("0-30Y", i) for i in range(30)]
        + [("30Y - 40Y", i) for i in range(30, 40)]
        + [("40Y - 50Y", i) for i in range(40, 50)]
        + [("50Y+", i) for i in range(50, 200)],
        columns=["age group", "age"],
    ),
    keys=["age"],
    store_name="Age Groups",
)

customer_store.join(age_groups_store)
Raghav Vashisht
  • 81
  • 4