I'm running the following notebook in zeppelin:
%spark.pyspark
l = [('user1', 33, 1.0, 'chess'), ('user2', 34, 2.0, 'tenis'), ('user3', None, None, ''), ('user4', None, 4.0, ' '), ('user5', None, 5.0, 'ski')]
df = spark.createDataFrame(l, ['name', 'age', 'ratio', 'hobby'])
df.show()
root
|-- name: string (nullable = true)
|-- age: long (nullable = true)
|-- ratio: double (nullable = true)
|-- hobby: string (nullable = true)
+-----+----+-----+-----+
| name| age|ratio|hobby|
+-----+----+-----+-----+
|user1| 33| 1.0|chess|
|user2| 34| 2.0|tenis|
|user3|null| null| |
|user4|null| 4.0| |
|user5|null| 5.0| ski|
+-----+----+-----+-----+
agg_df = df.select(*[(1.0 - (count(c) / count('*'))).alias(c) for c in df.columns])
agg_df.show()
root
|-- name: string (nullable = true)
|-- age: long (nullable = true)
|-- ratio: double (nullable = true)
|-- hobby: string (nullable = true)
+----+---+-------------------+-----+
|name|age| ratio|hobby|
+----+---+-------------------+-----+
| 0.0|0.6|0.19999999999999996| 0.0|
+----+---+-------------------+-----+
Now, I want to select in agg_df only columns which value is < 0.35. In this case it should return ['name', 'ratio', 'hobby']
I can't figure out how to do it. Any hint?