I hava two dataframes:
df1:
c1 c2 c3
1 192 1
3 192 2
4 193 3
5 193 3
7 193 5
9 194 7
df2:
v1
192
193
194
I want to add new column in df2, the result is:
df2:
v1 v2
192 2
193 2
194 1
explanation: v1=193, in df1, there are 3 rows, and the corresponding c3 are 3 \ 3 \ 5 the distinct value are 3 and 5, the count is 2, so the v2 in df2 is 2
Thank you, python version is best.
You can do a join, group by v1 and get the distinct count of c3.
import pyspark.sql.functions as F
result = (df1.join(df2, df1.c2 == df2.v1)
.groupBy('v1')
.agg(F.countDistinct('c3').alias('v2'))
)
result.show()
+---+---+
| v1| v2|
+---+---+
|193| 2|
|192| 2|
|194| 1|
+---+---+
You can try as below:
from pyspark.sql.types import *
from pyspark.sql.functions import *
sdf1 = spark.createDataFrame([
(1,192,1),
(3,192,2),
(4,193,3),
(5,193,3),
(7,193,5),
(9,194,7)
], ["c1", "c2", "c3"])
df2 = spark.createDataFrame([
(192,),
(193,),
(194,)
], ["v1"])
df1 = sdf1.groupBy("c2").agg(countDistinct("c3").alias("cnt"))
df2.join(df1, df1.c2 == df2.v1).select(df2.v1,df1.cnt).show()
