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, you 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('SparkByExamples.com').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 |
+-------------+----------+------+
In the above table, record with employer name James
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.
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. Source Code to Get Distinct Rows
import pyspark
from pyspark.sql import SparkSession
from pyspark.sql.functions import expr
spark = SparkSession.builder.appName('SparkByExamples.com').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)
}
The complete example is available at GitHub for reference.
Conclusion
In this PySpark SQL article, you have learned distinct()
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.
Happy Learning !!
Related Articles:
- PySpark count() – Different Methods Explained
- How to Convert PySpark Column to List?
- PySpark Union and UnionAll Explained
- PySpark – Drop One or Multiple Columns From DataFrame
- PySpark Groupby Count Distinct
- PySpark Count Distinct from DataFrame
- PySpark distinct vs dropduplicates
- PySpark Select Distinct Rows
I don’t see duplicate() method used, is there a confusion between distinct() and duplicate() ? Please check.
Thanks Sneha. Yes, it should be distinct(), there is no duplicate but PySpark also has dropDuplicates().
bro please correct them above if there is no duplicate , i spent alot time on this and after all i came to see if there is someone else having same problem
Hi Abdulsattar, I have updated the article when it was pointed out the first time. You should not see duplicate() function used anywhere. Could you please let me know where you are seeing, may be it’s caching somewhere.
If possible, please add “Next” button that takes us to the next Article.
That would be great.
Keep rocking
Thanks for the great article. For dropDuplicates(), is there a way to make a policy for which row is kept rather than just the first one every time?
Hi Isaac, I don’t think there is out of a box solution. But wondering how does it matter which one to keep when both rows are exactly same?
In your example, you call out Robert as being the one that is duplicated, but in your data example, it is James that is duplicated.
Thanks, Zeth. I have corrected it.