How do we define a grouped normalization function operating over partition DataFrames in Pandas?
Applying a Pandas DataFrame transformation across groups using groupby().applyInPandas().
Per-group statistical normalization, localized time-series ARIMA fitting, and group-wise ML modeling.
import pandas as pd
from pyspark.sql.functions import pandas_udf
def normalize_group(pdf: pd.DataFrame) -> pd.DataFrame:
pdf["score"] = (pdf["score"] - pdf["score"].mean()) / pdf["score"].std()
return pdfPractice typing production-grade PySpark code for Grouped Map Pandas UDF Concepts.