Q:

How to use lambda in agg and groupBy when using pyspark?

I am just studying pyspark. I am got confused about the following code:
df.groupBy(['Category','Register']).agg({'NetValue':'sum',
                                     'Units':'mean'}).show(5,truncate=False)

df.groupBy(['Category','Register']).agg({'NetValue':'sum',
                                     'Units': lambda x: pd.Series(x).nunique()}).show(5,truncate=False)

The first line is correct. But the second line is incorrect. The error message is:
AttributeError: 'function' object has no attribute '_get_object_id'

It looks like I did not use lambda function correctly. But this is how I use lambda in a normal python environment, and it is correct.
Could anyone help me here?

A:

If you are okay with the performance of PySpark primitives using pure Python functions, the following code gives the desired result.  You can modify the logic in _map to suit your specific need.  I made some assumptions about what your data schema might look like.
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, LongType

schema = StructType([
    StructField('Category', StringType(), True),
    StructField('Register', LongType(), True),
    StructField('NetValue', LongType(), True),
    StructField('Units', LongType(), True)
])

test_records = [
    {'Category': 'foo', 'Register': 1, 'NetValue': 1, 'Units': 1},
    {'Category': 'foo', 'Register': 1, 'NetValue': 2, 'Units': 2},
    {'Category': 'foo', 'Register': 2, 'NetValue': 3, 'Units': 3},
    {'Category': 'foo', 'Register': 2, 'NetValue': 4, 'Units': 4},
    {'Category': 'bar', 'Register': 1, 'NetValue': 5, 'Units': 5}, 
    {'Category': 'bar', 'Register': 1, 'NetValue': 6, 'Units': 6}, 
    {'Category': 'bar', 'Register': 2, 'NetValue': 7, 'Units': 7},
    {'Category': 'bar', 'Register': 2, 'NetValue': 8, 'Units': 8}
]

spark = SparkSession.builder.getOrCreate()
dataframe = spark.createDataFrame(test_records, schema)

def _map(((category, register), records)):
    net_value_sum = 0
    uniques = set()
    for record in records:
        net_value_sum += record['NetValue']
        uniques.add(record['Units'])
    return category, register, net_value_sum, len(uniques)

new_dataframe = spark.createDataFrame(
    dataframe.rdd.groupBy(lambda x: (x['Category'], x['Register'])).map(_map),
    schema
)
new_dataframe.show()

Result:
+--------+--------+--------+-----+
|Category|Register|NetValue|Units|
+--------+--------+--------+-----+
|     bar|       2|      15|    2|
|     foo|       1|       3|    2|
|     foo|       2|       7|    2|
|     bar|       1|      11|    2|
+--------+--------+--------+-----+

If you need performance or to stick with the pyspark.sql framework, then see this related question and its linked questions:
Custom aggregation on PySpark dataframes

