Pyspark join duplicate rows If you join on columns, you get duplicated columns. Apr 5, 2022 · In this article, we are going to learn how to duplicate a row N times in a PySpark DataFrame. remove either one one of these: Sep 23, 2024 · Hello I've seen posts that show how to remove duplicates, something like this: MERGE into [deltatable] as target USING ( select *, ROW_NUMBER() OVER (Partition By [primary keys] Order By [date] desc) as rn from [deltatable] qualify rn> 1 ) as source ON [merge primary keys and date column between so Nov 7, 2023 · You can use the following syntax to count the number of duplicate rows in a PySpark DataFrame: import pyspark. also, you will learn how to eliminate the duplicate columns on the result DataFrame. Depending on your needs, this may be sufficient. The following tutorials explain how to perform other common tasks in PySpark: PySpark: Get Rows Which Are Not in Another DataFrame PySpark: How to Combine Rows with Same Column Values PySpark: How to Drop Duplicate Rows from DataFrame Mar 27, 2024 · 1. One way to exploit this function is to use a udf to create a list of size n for each row. This requires careful condition design to avoid pairing a row with itself and to handle nulls. count() Nov 7, 2023 · The end result is that each row is repeated 3 times. It’s a transformation operation, meaning it’s lazy; Spark plans the union but waits for an action like show to execute it. joining two dataframes having duplicate row. For this, we are using dropDuplicates() method: Syntax: dataframe. dropDuplicates(subset=[col_name1, col_name2]) Edit for the comment May 12, 2024 · In PySpark SQL, an inner join is used to combine rows from two or more tables based on a related column between them. show() Use Case: Best for quick operations where you only need to remove duplicate columns after a join. This can be achieved using the `dropDuplicates` method available in PySpark, Scala, and Java. exceptAll(df. on str, list or Column, optional. drop() to remove unwanted duplicate columns. Usage of Effectively Create Duplicate Rows in Polars. dropDuplicates(['id', 'name']) . distinct() and either row 5 or row 6 will be removed. builder. a LEFT JOIN) left, leftouter, left_outer Fix: Use a left join to include rows with null join keys, then handle nulls post-join. However, if you'd like to keep all of the rows, you can use a Window function like shown in the other answers OR you can use a join() : Jan 20, 2024 · Removing duplicate rows or data using Apache Spark (or PySpark), can be achieved in multiple ways by using operations like drop_duplicate, distinct and groupBy. Apr 19, 2020 · How to remove 'duplicate' rows from joining the same pyspark dataframe? 0. dropDuplicates# DataFrame. count() and df. Jun 17, 2021 · These repeated values in our dataframe are called duplicate values. join(bb_df,'id', 'left'). Feb 18, 2025 · Special Shortcut for Equi-Join: If both DataFrames have the same column names and you want to perform an equi-join (i. customer_id) df. join(cc_df, 'id', 'left'). Sep 5, 2024 · Suppose you have two DataFrames (`df1` and `df2`) that you need to join, and both DataFrames have a column named “id”. If it return any row, it will show you which fieldx value has duplicates on table2. Let's use the collect_list() method to eliminate all the rows with duplicate letter1 and letter2 rows in the DataFrame and collect all the number1 entries as a list. unionByName(df2) will always produce a dataframe whose first N rows are df1's? The union method in PySpark DataFrames combines two or more DataFrames by stacking their rows vertically, returning a new DataFrame with all rows from the input DataFrames. For a static batch DataFrame, it just drops duplicate rows. How to remove 'duplicate' rows from joining the same pyspark dataframe? 0. groupBy(df. Thanks, Sanjay DataFrame. When should you join vs union DataFrames? Use joins when you need to match rows across DataFrames; Use unions when simply combining datasets without matching rows; Joins can duplicate data while unions deduplicate by default. a string for the join column name, a list of column names, a join expression (Column), or a list of Columns. Example: Jul 2, 2019 · You can start with this test. sql import Sp May 15, 2015 · I would like to remove duplicate rows based on the values of the first, third and fourth columns only. Removing entirely duplicate rows is straightforward: data = data. Below are the key approaches with examples. 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. and drop duplicates based the min row after grouping on all the columns you are interested in Apr 19, 2021 · A brute solution would be to just duplicate the rows of df2 the number of times the corresponding id appears in df1 and then do a normal outer join, but I think there must be a way to get the desired result by using joins. This article and notebook demonstrate how to perform a join so that you don’t have duplicated columns. Get Duplicate rows in pyspark using groupby count function – Keep or extract duplicate Nov 30, 2022 · primary_key = ['col_1', 'col_2'] duplicate_records = df. dropDuplicates() If want to drop duplicates from certain column. , join based on columns with the same name), you can directly pass the column name in the join condition. Join on columns. In this case, PySpark will automatically eliminate the duplicate column in the resulting DataFrame. show() where, SQL Join Clause PySpark Join Type Description; INNER JOIN: inner: Returns all rows when there is at least one match in BOTH tables: CROSS JOIN: cross: Returns all rows from the left table multiplied by all rows from the right table (Cartesian product) LEFT OUTER JOIN (a. e. dropDuplicates(['column 1','column Feb 18, 2018 · I need to show a dataframe made by three columns. show() Method 2: Find Duplicate Rows Across Specific Columns May 1, 2018 · You can count the number of distinct rows on a set of columns and compare it with the number of total rows. dropDuplicate(subset=col_name) For multiple columns: df. Nov 6, 2023 · Here’s how to handle duplicate rows and specific column duplicates in Spark, with detailed examples. df1. . The explode function returns a new row for each element in the given array or map. #display rows that have duplicate values across all columns df. If they are the same, there is no duplicate rows. Â Let's create a sample Dataframe Python3 # importing module import pyspark # importing sparksession from # pyspark. Removing Duplicate Rows. • We keep only the first occurrence, filtering out Sep 30, 2024 · PySpark SQL Left Outer Join, also known as a left join, combines rows from two DataFrames based on a related column. We can also assign a flag which indicates the duplicate records which is nothing but flagging duplicate row or getting indices of the duplicate rows in pyspark there by check if duplicate row is present. dropDuplicates()). The choice of operation to remove… Sep 19, 2024 · Code Example in PySpark Another approach is to drop the duplicate columns after the join operation by explicitly selecting the columns you want to retain Apr 28, 2020 · LeftOuter join will get all the rows from left table and matching rows from right table. Unions simply append rows without matching on keys. Then explode the resulting ar May 12, 2024 · PySpark Join is used to combine two DataFrames and by chaining these you can join multiple DataFrames; it supports all basic join type operations available in traditional SQL like INNER, LEFT OUTER, RIGHT OUTER, LEFT ANTI, LEFT SEMI, CROSS, SELF JOIN. I'm trying to do an inner join with 2 datasets: one has 2455 rows and the other over 1 million. sql module from pyspark. drop(orders. Using DROP in PySpark. For example, to remove duplicate rows based on the id and name columns, you would use the following code: dataframe. An inner join combines rows from two DataFrames where the join condition matches, excluding non-matching rows. Creating duplicate rows in Polars refers to adding copies of existing rows within a DataFrame, causing one or more rows to appear multiple times. To drop duplicates considering all columns: df. Pyspark: Join 2 dataframes with different number of rows by duplication. Both of these should be strings. dropDuplicates["id"] keeps the first one instead of latest. If a row in one table has no corresponding match in the other table, null values are filled in for the missing columns. org Mar 27, 2024 · PySpark DataFrame has a join() operation which is used to combine fields from two or multiple DataFrames (by chaining join()), in this article, you will learn how to do a PySpark Join on Two or Multiple DataFrames by applying conditions on the same or different columns. Aug 28, 2024 · Hi, I am trying to remove duplicate records from pyspark dataframe and keep the latest one. Join in pyspark without duplicate columns. Example in PySpark Apr 24, 2024 · Duplicate rows could be remove or drop from Spark SQL DataFrame using distinct() and dropDuplicates() functions, distinct() can be used to remove rows Aug 1, 2016 · Join real-time conversations, regardless of your reputation score. Below are the key approaches with detailed explanations and examples. dropduplicates(): Pyspark dataframe provides dropduplicates() function that is used to drop duplicate occurrences of data inside a dataframe. Sep 19, 2024 · When using Apache Spark, you may often encounter situations where you need to remove duplicate records from a DataFrame while keeping the first occurrence of each duplicate. Create the first dataframe for demonstration:Python3 # Importing necessary libraries from pyspark. Nov 14, 2020 · How can I keep the rows that came from the left table when dropping duplicates after a full join? I want to have all rows of both tables, except in cases where there are duplicates, then I throw away the row from the right table. join(orders, "customer_id", "inner"). Here’s how you can perform the join and remove the duplicate “id” column. In this method, we will first make a PySpark DataFrame using createDataFrame(). Additional Resources. window import Window from pyspark. I have tried the below, but without success: Nov 4, 2022 · imo simple inner join will be ok here, it is going to filter records from df1 with names which do not exists in df2 which is what you expect. Inner join: This is the most common type of join, where rows from both DataFrames are combined only when they have matching keys in the specified columns. Select the necessary columns explicitly to avoid duplicates. Pyspark: Mar 14, 2024 · This will return a new DataFrame with duplicate rows removed. Choosing the right join type depends on the nature of your data and Jan 30, 2025 · 1. join(df2,d1("name") === d2("name"),"inner") In the title you asked about duplicates, duplicated record are going to stay there after inner join, if you want to remove them you can use distinct Oct 23, 2023 · There are two common ways to find duplicate rows in a PySpark DataFrame: Method 1: Find Duplicate Rows Across All Columns. If the number of distinct rows is less than the total number of rows, duplicates exist. Advanced Self-Join for Duplicate Detection. All rows from the left DataFrame (the “left” side) are included in the result DataFrame, regardless of whether there is a matching row in the right DataFrame (the “right” side). result_df = aa_df. If working in PySpark, we can use . DataFrame. The inner join selects rows from both tables where the specified condition is satisfied, meaning it only includes rows that have matching values in the specified column(s) from both tables. If on is a string or a list of strings indicating the name of the join column(s), the column(s) must exist on both sides, and this performs an equi-join. functions as F df. Ask Question Asked 5 years, I am trying to join 2 pyspark dataframes by 2 columns, the See full list on geeksforgeeks. functions import row_number # apply window Shuffle Hash Join Dec 16, 2021 · In this article, we are going to drop the duplicate rows based on a specific column from dataframe using pyspark in Python. sql import SparkSession # Create a spark session spark = SparkSession. dropDuplicates(primary_key)) duplicate_records. This method operates on a DataFrame and allows you to specify one or more columns based on which duplicates should be identified and removed Sep 25, 2024 · A full outer join in PySpark SQL combines rows from two tables based on a matching condition, including all rows from both tables. If you have duplicates on table2, when you join it with table1 it will render duplicate rows of table1 (one for each relative value on table2). , that df1. Following is the sample dataset: # Prepare Data data - 19818 Oct 13, 2022 · If you perform a join in Spark and don’t specify your join correctly you’ll end up with duplicate column names. dropDuplicates (subset = None) [source] # Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. Apr 7, 2025 · In this article, we are going to drop the duplicate rows by using distinct() and dropDuplicates() functions from dataframe using pyspark in Python. select(list_of_columns). But how do I only remove duplicate rows based on columns 1, 3 and 4 only? I. This shouldn't return any rows. In our example, the column "Y" has a numerical value that can only be used here to repeat rows. This makes it harder to select those columns. Various Ways to Use Join in PySpark. To remove duplicate rows in Spark, you can use the dropDuplicates method. SELECT fieldx FROM table2 GROUP BY fieldx HAVING COUNT(1 May 11, 2018 · Please see the docs : withColumnRenamed() You need to pass the name of the existing column and the new name to the function. functions import col df = customers. Self-joins are often used to detect duplicates or near-duplicates by comparing rows within the same DataFrame. To handle duplicate values, we may use a strategy in which we keep the first occurrence of the values and drop the rest. Rows without matches in either DataFrame are not included in the Various Ways to Drop Duplicates in PySpark. dropDuplicates(['column 1','column 2','column n']). sql. 0. SELECT * FROM a JOIN b ON joinExprs If you want to ignore duplicate columns just drop them or select columns of interest afterwards. Fortunately, PySpark provides some methods to identify and remove duplicate rows from a DataFrame, ensuring that the data is clean and ready for analysis. Example Right side of the join. 2. distinct(). Oct 16, 2024 · When working with large datasets in PySpark, it's common to encounter duplicate records that can skew your analysis or cause issues in downstream processing. Mar 28, 2023 · We’ll cover inner, outer (full outer), left outer (left), right outer (right), and some more advanced join types in PySpark. Feb 2, 2024 · Below are some tips for dealing with duplicates using PySpark and SQL. PySpark Joins are wider transformations that involve data shuffling across the network. May 5, 2025 · Create a column with lists, and then use explode() to expand the lists into individual rows. appName('pyspark \ - exa Nov 30, 2022 · Solved: Hi, I need to find all occurrences of duplicate records in a PySpark DataFrame. . Feb 27, 2025 · 🔥 The Problem: Deduplicating Billion-Row Datasets in PySpark. Oct 14, 2024 · PySpark’s join operations are highly efficient for distributed computing, making it easy to merge data across large datasets. Method 1: Repeating rows based on column value. Consider following pyspark example remove duplicate from DataFrame using row_number window Function. join method is equivalent to SQL join like this. how str, optional Jun 15, 2018 · The groupBy() will have the consequence of dropping the duplicate rows. Collapsing records. Step-by-Step Solution (PySpark) 1. Inner Join. The dropDuplicates operation provides multiple methods to remove duplicate rows, each tailored to specific scenarios. In this article, we’ll explore two methods to remove duplicates from a PySpark Kontext Intelligent Platform - Code Snippets & Tips Killing duplicates is similar to dropping duplicates, just a little more aggressive. Nov 29, 2022 · The row_number() window function returns a sequential number starting from 1 within a window partition. Two of them show the names of someone who worked in a common movie (indicated by code on the third code) here's my code for the query: name_data_ Apr 4, 2018 · In order to remove any duplicate rows, just use union() followed by a distinct(). columns) This produces a cartesian product of join keys. Jul 8, 2019 · Join of 2 pysaprk dataframes and remove duplicates rows from the join. You can also specify which columns to use to identify duplicates by passing a list of column names to the dropDuplicates() function. Dec 16, 2021 · In this article, we will discuss how to remove duplicate columns after a DataFrame join in PySpark. One of the option is to use pandas drop_duplicates, Is there any solution in pyspark. from pyspark. Dropping Duplicates Across All Columns Thereby we keep or get duplicate rows in pyspark. All duplicates values will have row number other then 1. assigns a unique number to each duplicate row within a partition. Scala May 7, 2025 · Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. Learn to filter duplicate rows in PySpark DataFrames with Python SQL and optimization tips Master deduplication for clean efficient big data ETL pipelines I am using PySpark in Jupyter Notebook. show() The output will be: As you can see, I don't get all occurrences of duplicate records based on the Primary Key since one instance of duplicate records is present in "df. Can I trust that unionByName() will preserve the order of the rows, i. Remove duplicate rows from pyspark dataframe which have same value but in different column. Duplicate data means the same data based on some condition (column values). For a streaming DataFrame, it Jan 6, 2025 · Join two DataFrames in PySpark with ease, leveraging inner, outer, and left join types, and optimize data processing using efficient merge techniques, handling duplicate rows and null values. But somehow df. k. df. 3. Perform the join operation. Why is the… Jun 6, 2021 · In this article, we are going to drop the duplicate rows based on a specific column from dataframe using pyspark in Python. The join operation offers multiple ways to combine DataFrames, each tailored to specific needs. If you want to disambiguate you can use access these using parent DataFrames: val a: DataFrame = ??? val b: DataFrame = ??? val joinExprs: Column = ??? Jun 28, 2022 · I am trying to stack two dataframes (with unionByName()) and, then, dropping duplicate entries (with drop_duplicates()). 1. pyspark. withColumnRenamed('status', 'user_status') Join real-time conversations, regardless of your reputation score. dropDuplicates(primary_key)". kegr gcjctap swraqb bwop ups xbcxph txpxs qyicl cxprlo ecybjxn