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PySpark – distinct() and dropDuplicates()

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PySpark distinct() function is used to drop/remove the duplicate rows (all columns) from DataFrame and dropDuplicates() is used to drop rows based on selected (one or multiple) columns. In this article, we will learn how to use distinct() and dropDuplicates() functions with PySpark example.

Before we start, first let’s create a DataFrame with some duplicate rows and values on a few columns. We use this DataFrame to demonstrate how to get distinct multiple columns.

# Import pySpark
from pyspark.sql import SparkSession
from pyspark.sql.functions import expr

# Create SparkSession
spark = SparkSession.builder.appName('mytechmint').getOrCreate()

# Prepare Data
data = [("James", "Sales", 3000), \
    ("Michael", "Sales", 4600), \
    ("Robert", "Sales", 4100), \
    ("Maria", "Finance", 3000), \
    ("James", "Sales", 3000), \
    ("Scott", "Finance", 3300), \
    ("Jen", "Finance", 3900), \
    ("Jeff", "Marketing", 3000), \
    ("Kumar", "Marketing", 2000), \
    ("Saif", "Sales", 4100) \
  ]

# Create DataFrame
columns= ["employee_name", "department", "salary"]
df = spark.createDataFrame(data = data, schema = columns)
df.printSchema()
df.show(truncate=False)

Yields below output

+-------------+----------+------+
|employee_name|department|salary|
+-------------+----------+------+
|James        |Sales     |3000  |
|Michael      |Sales     |4600  |
|Robert       |Sales     |4100  |
|Maria        |Finance   |3000  |
|James        |Sales     |3000  |
|Scott        |Finance   |3300  |
|Jen          |Finance   |3900  |
|Jeff         |Marketing |3000  |
|Kumar        |Marketing |2000  |
|Saif         |Sales     |4100  |
+-------------+----------+------+

On the above table, record with employer’s name Robert has duplicate rows, As you notice we have 2 rows that have duplicate values on all columns and we have 4 rows that have duplicate values on department and salary columns.

Related:  PySpark - DateTime Functions

1. Get Distinct Rows (By Comparing All Columns)

On the above DataFrame, we have a total of 10 rows with 2 rows having all values duplicated, performing distinct on this DataFrame should get us 9 after removing 1 duplicate row.

distinctDF = df.distinct()
print("Distinct count: "+str(distinctDF.count()))
distinctDF.show(truncate=False)

distinct() function on DataFrame returns a new DataFrame after removing the duplicate records. This example yields the below output.

Distinct count: 9
+-------------+----------+------+
|employee_name|department|salary|
+-------------+----------+------+
|James        |Sales     |3000  |
|Michael      |Sales     |4600  |
|Maria        |Finance   |3000  |
|Robert       |Sales     |4100  |
|Saif         |Sales     |4100  |
|Scott        |Finance   |3300  |
|Jeff         |Marketing |3000  |
|Jen          |Finance   |3900  |
|Kumar        |Marketing |2000  |
+-------------+----------+------+

Alternatively, you can also run dropDuplicates() function which returns a new DataFrame after removing duplicate rows.

df2 = df.dropDuplicates()
print("Distinct count: "+str(df2.count()))
df2.show(truncate=False)

2. PySpark Distinct of Selected Multiple Columns

PySpark doesn’t have a distinct method that takes columns that should run distinct on (drop duplicate rows on selected multiple columns) however, it provides another signature of dropDuplicates() function which takes multiple columns to eliminate duplicates.

Note that calling dropDuplicates() on DataFrame returns a new DataFrame with duplicate rows removed.

dropDisDF = df.dropDuplicates(["department","salary"])
print("Distinct count of department & salary : "+str(dropDisDF.count()))
dropDisDF.show(truncate=False)

Yields below output. If you notice the output, It dropped 2 records that are duplicates.

Distinct count of department & salary : 8
+-------------+----------+------+
|employee_name|department|salary|
+-------------+----------+------+
|Jen          |Finance   |3900  |
|Maria        |Finance   |3000  |
|Scott        |Finance   |3300  |
|Michael      |Sales     |4600  |
|Kumar        |Marketing |2000  |
|Robert       |Sales     |4100  |
|James        |Sales     |3000  |
|Jeff         |Marketing |3000  |
+-------------+----------+------+

3. More Examples to Get Distinct Rows

import pyspark
from pyspark.sql import SparkSession
from pyspark.sql.functions import expr
spark = SparkSession.builder.appName('mytechmint').getOrCreate()

data = [("James", "Sales", 3000), \
    ("Michael", "Sales", 4600), \
    ("Robert", "Sales", 4100), \
    ("Maria", "Finance", 3000), \
    ("James", "Sales", 3000), \
    ("Scott", "Finance", 3300), \
    ("Jen", "Finance", 3900), \
    ("Jeff", "Marketing", 3000), \
    ("Kumar", "Marketing", 2000), \
    ("Saif", "Sales", 4100) \
  ]
columns= ["employee_name", "department", "salary"]
df = spark.createDataFrame(data = data, schema = columns)
df.printSchema()
df.show(truncate=False)

#Distinct
distinctDF = df.distinct()
print("Distinct count: "+str(distinctDF.count()))
distinctDF.show(truncate=False)

#Drop duplicates
df2 = df.dropDuplicates()
print("Distinct count: "+str(df2.count()))
df2.show(truncate=False)

#Drop duplicates on selected columns
dropDisDF = df.dropDuplicates(["department","salary"])
print("Distinct count of department salary : "+str(dropDisDF.count()))
dropDisDF.show(truncate=False)
}

Conclusion

In this PySpark SQL article, wehave learned distinct() a method which is used to get the distinct values of rows (all columns) and also learned how to use dropDuplicates() to get the distinct and finally learned using dropDuplicates() function to get distinct of multiple columns.

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