pyspark.sql.functions.pandas_udf
PySpark’s PandasUDFType is a type of user-defined function (UDF) that allows you to use the power of Pandas library with PySpark dataframes. It allows you to define a UDF that takes one or more columns of a PySpark dataframe as input and returns a Pandas dataframe as output.
For example, you can use a PandasUDFType to calculate the moving average of a column in a PySpark dataframe as follows:
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.types import DoubleType
# Define the UDF
@pandas_udf(DoubleType())
def moving_average(values, window):
return values.rolling(window).mean()
# Use the UDF in a PySpark dataframe
df = spark.createDataFrame([(1, 2, 3, 4), (4, 5, 6, 7), (7, 8, 9, 10)], ["col1", "col2", "col3", "col4"])
df.select("col1", moving_average("col1", 2).over(Window.rowsBetween(-1, 1)).alias("moving_avg")).show()
In this example, the UDF moving_average takes two arguments: values and window. values is the column of the PySpark dataframe that we want to calculate the moving average of, and window is the window size for the moving average. The UDF then returns the rolling mean of values with the given window size. The UDF is then used in a PySpark dataframe df with window size 2 to calculate the moving average of column col1 and it returns the rolling mean of col1 with window size 2.
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