Reputation: 95
I'm using pyspark dataframe with a goal to get counts of a variable which can be in multiple columns. Wrote a sql query to get this but unable to translate it for dataframes.
Given the below dataframe, need to get counts of "Foo", "Bar", "Air" in Col1, Col2.
+----------+----+-----+
| ID |Col1|Col2 |
+----------+----+-----+
|2017-01-01| Air| Foo |
|2017-01-02| Foo| Bar|
|2017-01-03| Bar| Air |
|2017-01-04| Air| Foo|
|2017-01-09| Bar| Foo|
|2017-01-01|Foo | Bar|
|2017-01-02|Bar | Air|
|2017-01-01|Foo | Air|
|2017-01-02|Foo | Air|
+----------+----+-----+
Expected output
+-------+-----+
|Var . |Count|
+-------+-----+
| Foo| 7 |
| Air| 6 |
| Bar| 5 |
+-------+-----+
Upvotes: 0
Views: 896
Reputation: 2655
Try this:
Creating DataFrame
import pyspark.sql.functions as f
l1 = [('2017-01-01','Air','Foo'),
('2017-01-02','Foo','Bar'),
('2017-01-03','Bar','Air'),
('2017-01-04','Air','Foo'),
('2017-01-09','Bar','Foo'),
('2017-01-01','Foo','Bar'),
('2017-01-02','Bar','Air'),
('2017-01-01','Foo','Air'),
('2017-01-02','Foo','Air')]
df = spark.createDataFrame(l1).toDF('id', 'col1', 'col2')
df.show()
+----------+----+----+
| id|col1|col2|
+----------+----+----+
|2017-01-01| Air| Foo|
|2017-01-02| Foo| Bar|
|2017-01-03| Bar| Air|
|2017-01-04| Air| Foo|
|2017-01-09| Bar| Foo|
|2017-01-01| Foo| Bar|
|2017-01-02| Bar| Air|
|2017-01-01| Foo| Air|
|2017-01-02| Foo| Air|
+----------+----+----+
First concat col1
and col2
with ,
as a separator. Split the column by ,
and then explode will give row for each word.
df.withColumn('col', f.explode(f.split(f.concat('col1',f.lit(','),'col2'),','))).groupBy('col').count().show()
+---+-----+
|col|count|
+---+-----+
|Bar| 5|
|Foo| 7|
|Air| 6|
+---+-----+
Upvotes: 1