pyspark equivalent of pandas groupby + apply on column

user_2411173 0
Viewed 3147

I have a spark DataFrame and I would like to count the number of unique values for a variable after groupby.

In pandas I can obtain it as : df.groupby('UserName').apply(lambda x: x['Server'].nunique())

How can I get the same results when df is a pyspark dataframe?

pyspark dataframe group-by
2 Answers

You can use countDistinct with agg:

df.groupBy('UserName').agg(countDistinct('Server').alias('Server'))
user_49834500

So you can implement same logic like pandas.groupby().apply in pyspark using @pandas_udf and which is vectorization method and faster then simple udf.

from pyspark.sql.functions import pandas_udf,PandasUDFType

df3 = spark.createDataFrame(
[("a", 1, 0), ("a", -1, 42), ("b", 3, -1), ("b", 10, -2)],
("key", "value1", "value2")
)

from pyspark.sql.types import *

schema = StructType([
    StructField("key", StringType()),
    StructField("avg_value1", DoubleType()),
    StructField("avg_value2", DoubleType()),
    StructField("sum_avg", DoubleType()),
    StructField("sub_avg", DoubleType())
])

@pandas_udf(schema, functionType=PandasUDFType.GROUPED_MAP)
def g(df):
    gr = df['key'].iloc[0]
    x = df.value1.mean()
    y = df.value2.mean()
    w = df.value1.mean() + df.value2.mean()
    z = df.value1.mean() - df.value2.mean()
    return pd.DataFrame([[gr]+[x]+[y]+[w]+[z]])

df3.groupby("key").apply(g).show()

You can use all pandas direct functions inside def

You will get below result:

+---+----------+----------+-------+-------+
|key|avg_value1|avg_value2|sum_avg|sub_avg|
+---+----------+----------+-------+-------+
|  b|       6.5|      -1.5|    5.0|    8.0|
|  a|       0.0|      21.0|   21.0|  -21.0|
+---+----------+----------+-------+-------+

So , You can do more calculation between other fields in grouped data.and add them into dataframe in list format.

user_88307960
Related