GROUP BY with overlapping rows in PySpark SQL - sql

Question

The following table was created using Parquet / PySpark, and the objective is to aggregate rows where 1 < count < 5 and rows where 2 < count < 6. Note the row where count is 4.1 falls in both ranges.
+-----+-----+
|count|value|
+-----+-----+
| 1.1| 1|
| 1.2| 2|
| 4.1| 3|
| 5.5| 4|
| 5.6| 5|
| 5.7| 6|
+-----+-----+
Here is code to create and then read the above table as a PySpark DataFrame.
import pandas as pd
import pyarrow.parquet as pq
import pyarrow as pa
from pyspark import SparkContext, SQLContext
# create Parquet DataFrame
pdf = pd.DataFrame({
'count': [1.1, 1.2, 4.1, 5.5, 5.6, 5.7],
'value': [1, 2, 3, 4, 5, 6]})
table = pa.Table.from_pandas(pdf)
pq.write_to_dataset(table, r'c:/data/data.parquet')
# read Parquet DataFrame and create view
sc = SparkContext()
sql = SQLContext(sc)
df = sql.read.parquet(r'c:/data/data.parquet')
df.createTempView('data')
The operation can use two separate queries.
q1 = sql.sql("""
SELECT AVG(value) AS va
FROM data
WHERE count > 1
AND count < 5
""")
+---+
| va|
+---+
|2.0|
+---+
and, similarly
q2 = sql.sql("""
SELECT AVG(value) as va
FROM data
WHERE count > 2
AND count < 6
""")
+---+
| va|
+---+
|4.5|
+---+
However I want to do this in one efficient query.
Here is an approach that does not work because the row where count is 4.1 is included in only one group.
qc = sql.sql("""
SELECT AVG(value) AS va,
(CASE WHEN count > 1 AND count < 5 THEN 1
WHEN count > 2 AND count < 6 THEN 2
ELSE 0 END) AS id
FROM data
GROUP BY id
""")
The above query produces
+---+---+
| va| id|
+---+---+
|2.0| 1|
|5.0| 2|
+---+---+
To be clear the desired result is something more like
+---+---+
| va| id|
+---+---+
|2.0| 1|
|4.5| 2|
+---+---+

Answer 1

The simplest method is probably union all:
SELECT 1, AVG(value) AS va
FROM data
WHERE count > 1 AND count < 5
UNION ALL
SELECT 2, AVG(value) as va
FROM data
WHERE count > 2 AND count < 6;
You can also phrase this as:
select r.id, avg(d.value)
from data d join
(select 1 as lo, 5 as hi, 1 as id union all
select 2 as lo, 6 as hi, 2 as id
) r
on d.count > r.lo and d.count < r.hi
group by r.id;