I'm using pyspark(Python 2.7.9/Spark 1.3.1) and have a dataframe GroupObject which I need to filter & sort in the descending order. Trying to achieve it via this piece of code.
group_by_dataframe.count().filter("`count` >= 10").sort('count', ascending=False)
But it throws the following error.
sort() got an unexpected keyword argument 'ascending'
This question is related to
python
apache-spark
dataframe
pyspark
apache-spark-sql
In PySpark 1.3 sort method doesn't take ascending parameter. You can use desc method instead:
from pyspark.sql.functions import col
(group_by_dataframe
.count()
.filter("`count` >= 10")
.sort(col("count").desc()))
or desc function:
from pyspark.sql.functions import desc
(group_by_dataframe
.count()
.filter("`count` >= 10")
.sort(desc("count"))
Both methods can be used with with Spark >= 1.3 (including Spark 2.x).
you can use groupBy and orderBy as follows also
dataFrameWay = df.groupBy("firstName").count().withColumnRenamed("count","distinct_name").sort(desc("count"))
In pyspark 2.4.4
1) group_by_dataframe.count().filter("`count` >= 10").orderBy('count', ascending=False)
2) from pyspark.sql.functions import desc
group_by_dataframe.count().filter("`count` >= 10").orderBy('count').sort(desc('count'))
No need to import in 1) and 1) is short & easy to read,
So I prefer 1) over 2)
By far the most convenient way is using this:
df.orderBy(df.column_name.desc())
Doesn't require special imports.
Use orderBy:
df.orderBy('column_name', ascending=False)
Complete answer:
group_by_dataframe.count().filter("`count` >= 10").orderBy('count', ascending=False)
http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html
Source: Stackoverflow.com