Why do you want to use this function instead of the built-in stddev
function?
On Wed, Dec 23, 2020 at 2:52 PM Mich Talebzadeh <mich.talebza...@gmail.com>
wrote:
> Hi,
>
>
> This is a shot in the dark so to speak.
>
>
> I would like to use the standard deviation std offered by numpy in
> PySpark. I am using SQL for now
>
>
> The code as below
>
>
> sqltext = f"""
>
> SELECT
>
> rs.Customer_ID
>
> , rs.Number_of_orders
>
> , rs.Total_customer_amount
>
> , rs.Average_order
>
> , rs.Standard_deviation
>
> FROM
>
> (
>
> SELECT cust_id AS Customer_ID,
>
> COUNT(amount_sold) AS Number_of_orders,
>
> SUM(amount_sold) AS Total_customer_amount,
>
> AVG(amount_sold) AS Average_order,
>
> * STDDEV(amount_sold) AS Standard_deviation*
>
> FROM {DB}.{table}
>
> GROUP BY cust_id
>
> HAVING SUM(amount_sold) > 94000
>
> AND AVG(amount_sold) < STDDEV(amount_sold)
>
> ) rs
>
> ORDER BY
>
> 3 DESC
>
> """
>
> spark.sql(sqltext)
>
> Now if I wanted to use UDF based on numpy STD function, I can do
>
> import numpy as np
> from pyspark.sql.functions import UserDefinedFunction
> from pyspark.sql.types import DoubleType
> udf = UserDefinedFunction(np.std, DoubleType())
>
> How can I use that udf with spark SQL? I gather this is only possible
> through functional programming?
>
> Thanks,
>
> Mich
>
>
>
>
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