What's the equivalent of Panda's value_counts() in PySpark?

Question

I am having the following python/pandas command:

df.groupby('Column_Name').agg(lambda x: x.value_counts().max() 

where I am getting the value counts for ALL columns in a DataFrameGroupBy object.

How do I do this action in PySpark?

Answer 1

It's more or less the same:

spark_df.groupBy('column_name').count().orderBy('count') 

In the groupBy you can have multiple columns delimited by a ,

For example groupBy('column_1', 'column_2')

Answer 2

try this when you want to control the order:

data.groupBy('col_name').count().orderBy('count', ascending=False).show()