Is there a way in pyspark to count unique values

Question

I have a spark dataframe (12m x 132) and I am trying to calculate the number of unique values by column, and remove columns that have only 1 unique value.

So far, I have used the pandas nunique function as such:

import pandas as pd

df = sql_dw.read_table(<table>)
df_p = df.toPandas()

nun = df_p.nunique(axis=0)
nundf = pd.DataFrame({'atr':nun.index, 'countU':nun.values})

dropped = []
for i, j in nundf.values:
  if j == 1:
    dropped.append(i)
    df = df.drop(i)
print(dropped)

Is there a way to do this that is more native to spark - i.e. not using pandas?

Answer 1

Please have a look at the commented example below. The solution requires more python as pyspark specific knowledge.

import pyspark.sql.functions as F
#creating a dataframe
columns = ['asin' ,'ctx' ,'fo' ]

l = [('ASIN1','CTX1','FO1')
,('ASIN1','CTX1','FO1')
,('ASIN1','CTX1','FO2')
,('ASIN1','CTX2','FO1')
,('ASIN1','CTX2','FO2')
,('ASIN1','CTX2','FO2')
,('ASIN1','CTX2','FO3')
,('ASIN1','CTX3','FO1')
,('ASIN1','CTX3','FO3')]

df=spark.createDataFrame(l, columns)

df.show()
#we create a list of functions we want to apply
#in this case countDistinct for each column
expr = [F.countDistinct(c).alias(c) for c in df.columns]

#we apply those functions
countdf =  df.select(*expr)
#this df has just one row
countdf.show()

#we extract the columns which have just one value
cols2drop = [k for k,v in countdf.collect()[0].asDict().items() if v == 1]
df.drop(*cols2drop).show()

Output:

+-----+----+---+
| asin| ctx| fo|
+-----+----+---+
|ASIN1|CTX1|FO1|
|ASIN1|CTX1|FO1|
|ASIN1|CTX1|FO2|
|ASIN1|CTX2|FO1|
|ASIN1|CTX2|FO2|
|ASIN1|CTX2|FO2|
|ASIN1|CTX2|FO3|
|ASIN1|CTX3|FO1|
|ASIN1|CTX3|FO3|
+-----+----+---+

+----+---+---+
|asin|ctx| fo|
+----+---+---+
|   1|  3|  3|
+----+---+---+

+----+---+
| ctx| fo|
+----+---+
|CTX1|FO1|
|CTX1|FO1|
|CTX1|FO2|
|CTX2|FO1|
|CTX2|FO2|
|CTX2|FO2|
|CTX2|FO3|
|CTX3|FO1|
|CTX3|FO3|
+----+---+