How to count occurrences of each distinct value for every column in a dataframe?

Question

edf.select(\"x\").distinct.show() shows the distinct values that are present in x column of edf DataFrame.

Is there an efficient

Answer 1

df.select("some_column").distinct.count

Answer 2

Another option without resorting to sql functions

df.groupBy('your_column_name').count().show()

show will print the different values and their occurrences. The result without show will be a dataframe.

Answer 3

Roughly speaking, how it works:

Answer 4

countDistinct is probably the first choice:

import org.apache.spark.sql.functions.countDistinct

df.agg(countDistinct("some_column"))

If speed is more important than the accuracy you may consider approx_count_distinct (approxCountDistinct in Spark 1.x):

import org.apache.spark.sql.functions.approx_count_distinct

df.agg(approx_count_distinct("some_column"))

To get values and counts:

df.groupBy("some_column").count()

In SQL (spark-sql):

SELECT COUNT(DISTINCT some_column) FROM df

and

SELECT approx_count_distinct(some_column) FROM df

Answer 5

If you are using Java, the import org.apache.spark.sql.functions.countDistinct; will give an error : The import org.apache.spark.sql.functions.countDistinct cannot be resolved

To use the countDistinct in java, use the below format:

import org.apache.spark.sql.functions.*;
import org.apache.spark.sql.*;
import org.apache.spark.sql.types.*;

df.agg(functions.countDistinct("some_column"));

Answer 6

import org.apache.spark.sql.functions.countDistinct

df.groupBy("a").agg(countDistinct("s")).collect()