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GroupBy and CountDistinct are two powerful functions in PySpark that can be used to aggregate data. GroupBy allows you to group data by specific columns and then apply an aggregation function such as CountDistinct to the grouped data to count the number of unique values in the grouped data. This can be used to calculate the number of unique values in each group and can be helpful in understanding the data.
You can use the following syntax to count the number of distinct values in one column of a PySpark DataFrame, grouped by another column:
from pyspark.sql.functions import countDistinct
df.groupBy('team').agg(countDistinct('points')).show()
This particular example calculates the number of distinct values in the points column, grouped by the values in the team column.
The following example shows how to use this syntax in practice.
Example: How to Use groupBy with Count Distinct in PySpark
Suppose we have the following PySpark DataFrame that contains information about the points scored by various basketball players:
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
#define data
data = [['A', 'Guard', 11],
['A', 'Guard', 8],
['A', 'Forward', 22],
['A', 'Forward', 22],
['B', 'Guard', 14],
['B', 'Guard', 14],
['B', 'Forward', 13],
['B', 'Forward', 14],
['C', 'Forward', 23],
['C', 'Guard', 30]]
#define column names
columns = ['team', 'position', 'points']
#create dataframe using data and column names
df = spark.createDataFrame(data, columns)
#view dataframe
df.show()
+----+--------+------+
|team|position|points|
+----+--------+------+
| A| Guard| 11|
| A| Guard| 8|
| A| Forward| 22|
| A| Forward| 22|
| B| Guard| 14|
| B| Guard| 14|
| B| Forward| 13|
| B| Forward| 14|
| C| Forward| 23|
| C| Guard| 30|
+----+--------+------+
We can use the following syntax to calculate the number of distinct values in the points column, grouped by the values in the team column:
from pyspark.sql.functions import countDistinct
#calculate distinct values in points column, grouped by team column
df.groupBy('team').agg(countDistinct('points')).show()
+----+-------------+
|team|count(points)|
+----+-------------+
| B| 2|
| C| 2|
| A| 3|
+----+-------------+
The resulting DataFrame shows the number of distinct values in the points column, grouped by the values in the team column.
For example, we can see:
- There are 2 distinct values in the points column for team B.
- There are 2 distinct values in the points column for team C.
- There are 3 distinct values in the points column for team A.
If you would like to give the count(points) column a different name, you can use the alias function as follows:
from pyspark.sql.functions import countDistinct
#calculate distinct values in points column, grouped by team column
df.groupBy('team').agg(countDistinct('points').alias('distinct_points')).show()
+----+---------------+
|team|distinct_points|
+----+---------------+
| B| 2|
| C| 2|
| A| 3|
+----+---------------+
The resulting DataFrame shows the number of distinct points values for each team with the distinct column now named distinct_points, just as we specified in the alias function.
Note: You can find the complete documentation for the PySpark groupBy function .