2024 Left anti join pyspark

I am trying to get all rows within a dataframe where a columns value is not within a list (so filtering by exclusion). As an example: df = sqlContext.createDataFrame .... Custer road umc

PySpark Join Types. 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.Try a full outer join. This will join the dfs on a specific column returning all matching records from both tables whether the other table matches or not. Then where there are rows in df1 that don't have matches in df2 or vice versa, those rows will be listed as well but with nulls. So: Do full outer join and assign the result to a new df df_outerFirst - what does the Join Tool do? For now, the join tool does a simple inner join with an equal sign. That's it! In particular: • R output anchor is NOT the result of a right outer join. I know the R letter can make you think this but it is not. • Similarly: L output anchor is NOT a left outer join. I know that got me at first too!PySpark Joins with SQL. Use PySpark joins with SQL to compare, and possibly combine, data from two or more datasources based on matching field values. This is simply called “joins” in many cases and usually the datasources are tables from a database or flat file sources, but more often than not, the data sources are becoming Kafka topics.PySpark SQL Left Outer Join (left, left outer, left_outer) returns all rows from the left DataFrame regardless of the match found on the right DataFrame. ... When you join two DataFrames using Left Anti Join (leftanti), it returns only columns from the left DataFrame for non-matched records. In this PySpark article, I will explain how to do ...If you’re looking for a way to serve your country, the Air Force is a great option. To join, you must be an American citizen and meet other requirements, and once you’re a member, you help protect the country via the air. Take a look at the...how to do anti left join when the left dataframe is aggregated in pyspark Ask Question Asked 8 months ago Modified 8 months ago Viewed 48 times 0 I need to do anti left join and flatten the table. in the most efficient way possible because the right table is massive. so the first table is: like 1000-10,000 rowsTo do a cross-join operation in Power Query, first go to the Product table. From the Add column tab on the ribbon, select Custom column. More information: Add a custom column. In the Custom column dialog box, enter whatever name you like in the New column name box, and enter Colors in the Custom column formula box.I'm using Pyspark 2.1.0. I'm attempting to perform a left outer join of two dataframes using the following: I have 2 dataframes, schema of which appear as follows: crimes |-- CRIME_ID: string (Are you passionate about animation? Do you dream of bringing characters to life on screen? If so, then it’s time to take your skills to the next level by joining a free online animation course.Perform a left outer join of self and other. For each element (k, v) in self, the resulting RDD will either contain all pairs (k, (v, w)) for w in other, or the pair (k, (v, None)) if no elements in other have key k. Hash-partitions the resulting RDD into the given number of partitions.Pyspark Left Join may return more records Mohammad Younus Jameel 1y How to Shoot Your Shot in the DM Chidinma Eke, MBA, SPHRi, ACIPM 1y it's perfectly fine to shoot your shot on LinkedIn ...In addition, PySpark provides conditions that can be specified instead of the 'on' parameter. For example, if you want to join based on range in Geo Location-based data, you may want to choose ...join Description. You can use the join command to combine the results of a main search (left-side dataset) with the results of either another dataset or a subsearch (right-side dataset). You can also combine a search result set to itself using the selfjoin command.. The left-side dataset is the set of results from a search that is piped into the join command and then merged on the right side ...We start with two dataframes: dfA and dfB. dfA.join (dfB, 'user', 'inner') means join just the rows where dfA and dfB have common elements on the user column. (intersection of A and B on the user column). dfA.join (dfB, 'user', 'leftanti') means construct a dataframe with elements in dfA THAT ARE NOT in dfB. Are these two correct? sql.PySpark expr() is a SQL function to execute SQL-like expressions and to use an existing DataFrame column value as an expression argument to Pyspark built-in functions. Most of the commonly used SQL functions are either part of the PySpark Column class or built-in pyspark.sql.functions API, besides these PySpark also supports many other SQL functions, so in order to use these, you have to use ...I want to solve this using Anti-Join. Would the below code work for this purpose? SELECT * FROM table1 t1 LEFT JOIN table2 t2 ON t2.sender_id = t1.sender_id AND t2.event_date > t1.event_date WHERE t2.sender_id IS NULL Please feel free to suggest any method other than anti-join. Thanks!In addition to these basic join types, PySpark also supports advanced join types like left semi join, left anti join, and cross join. As you explore working with data in PySpark, you'll find these join operations to be critical tools for combining and analyzing data across multiple DataFrames. Merging DataFrames Using PySpark FunctionsI am trying to add leading zeroes to a column in my pyspark dataframe input :- ID 123 Output expected: 000000000123. Stack Overflow. About; Products For Teams; ... Left-pad the string column to width len with pad. from pyspark.sql.functions import lpad df.select(lpad(df.ID, 12, '0').alias('s')).collect() Share.When using PySpark, it's often useful to think "Column Expression" when you read "Column". Logical operations on PySpark columns use the bitwise operators: & for and. | for or. ~ for not. When combining these with comparison operators such as <, parenthesis are often needed. In your case, the correct statement is:Feb 20, 2023 · Below is an example of how to use Left Outer Join ( left, leftouter, left_outer) on PySpark DataFrame. From our dataset, emp_dept_id 6o doesn’t have a record on dept dataset hence, this record contains null on dept columns (dept_name & dept_id). and dept_id 30 from dept dataset dropped from the results. Below is the result of the above Join ... {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"resources","path":"resources","contentType":"directory"},{"name":"README.md","path":"README ...Spark DataFrame Full Outer Join Example. In order to use Full Outer Join on Spark SQL DataFrame, you can use either outer, full, fullouter Join as a join type. From our emp dataset’s emp_dept_id with value 60 doesn’t have a record on dept hence dept columns have null and dept_id 30 doesn’t have a record in emp hence you see null’s on ...PySpark optimize left join of two big tables. I'm using the most updated version of PySpark on Databricks. I have two tables each of the size ~25-30GB. I want to join Table1 and Table2 at the "id" and "id_key" columns respectively. I'm able to do that with the command below but when I run my spark job the join is skewed resulting in +95% of my ...1. Join operations are often used in a typical data analytics flow in order to correlate two data sets. Apache Spark, being a unified analytics engine, has also provided a solid foundation to execute a wide variety of Join scenarios. At a very high level, Join operates on two input data sets and the operation works by matching each of the data ...The syntax for PySpark join two dataframes. The syntax for PySpark join two dataframes function is:-. df = b. join ( d , on =['Name'] , how = 'inner') b: The 1 st data frame to be used for join. d: The 2 nd data frame to be used for join further. The Condition defines on which the join operation needs to be done.You can use the anti_join() function from the dplyr package in R to return all rows in one data frame that do not have matching values in another data frame. This function uses the following basic syntax: anti_join(df1, df2, by= ' col_name ') The following examples show how to use this syntax in practice. Example 1: Use anti_join() with One …Pyspark left anti join is simple opposite to left join. It shows the only those records which are not match in left join. In this article we will understand them with examples step by step. 2.1 Anti Join on Multiple Columns. Left anti join or anti join selects all rows from the left data frame that are not present in the right data frame (similar to left df - right df). To perform an anti join on multiple columns with the same names on both R data frames, use all the column names as a list to by param.I would like to perform an anti-join so that the resulting data frame contains the rows of df1 where the key [['label1', 'label2']] is not found in df2. The resulting df should be: label1 label2 value A b 2 B c 3 C d 4 In R using dplyr, the code would be:pyspark.sql.DataFrame.join. ¶. Joins with another DataFrame, using the given join expression. New in version 1.3.0. a string for the join column name, a list of column names, a join expression (Column), or a list of Columns. 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 ...4. Join on Column vs Merge on Column. merge () allows us to use columns in order to combine DataFrames and by default, it uses inner join. Below example by default join on the column as this is the only common column in both DataFrames. # pandas merge - inner join by Column df3=pd.merge (df1,df2){"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"resources","path":"resources","contentType":"directory"},{"name":"README.md","path":"README ...DataFrame.crossJoin(other) [source] ¶. Returns the cartesian product with another DataFrame. New in version 2.1.0. Parameters. other DataFrame. Right side of the cartesian product.I am using AWS Glue to join two tables. By default, it performs INNER JOIN. I want to do a LEFT OUTER JOIN. I referred the AWS Glue documentation but there is no way to pass the join type to the Join.apply() method. Is there a way to achieve this in AWS Glue?{"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"resources","path":"resources","contentType":"directory"},{"name":"README.md","path":"README ...Contribute to datawizzard/PySpark-Examples development by creating an account on GitHub.4. The answer to your issue should be. SELECT a.key FROM a LEFT OUTER JOIN b ON a.key = b.key WHERE b.key IS NULL; This means, bring all the keys from a, irrespective of whether there is a match in b or not. The where cause will filter those records, which are not available in b. Share.Join in Spark SQL is the functionality to join two or more datasets that are similar to the table join in SQL based databases. Spark works as the tabular form of datasets and data frames. The Spark SQL supports several types of joins such as inner join, cross join, left outer join, right outer join, full outer join, left semi-join, left anti join.I am doing a simple left outer join in PySpark and it is not giving correct results. Please see bellow. Value 5 (in column A) is between 1 (col B) and 10 (col C) that's why B and C should be in the output table in the first row. But I'm getting nulls. I've tried this in 3 different RDBMs MS SQL, PostGres, and SQLite all giving the correct results.In addition to these basic join types, PySpark also supports advanced join types like left semi join, left anti join, and cross join. As you explore working with data in PySpark, you’ll find these join operations to be critical tools for combining and analyzing data across multiple DataFrames. Merging DataFrames Using PySpark FunctionsLeft semi joins (as in ExampleÂ 4-9 and TableÂ 4-7) and left anti joins (as in TableÂ 4-8) are the only kinds of joins that only have values from the left table. A left semi join is the same as filtering the left table for only rows with keys present in the right table. The left anti join also only returns data from the left table, but ...Are you looking to reconnect with old friends and classmates? If so, joining Classmates Official Site may be the perfect way to do so. Classmates is a website that allows users to search for and connect with former classmates and friends.In this video, I discussed about left semi, left anti & self joins in PySparkLink for PySpark Playlist:https://www.youtube.com/watch?v=6MaZoOgJa84&list=PLMWa...Explanation. Lines 1–2: Import the pyspark and SparkSession. Line 4: We create a SparkSession with the application name edpresso. Lines 6–9: We define the dummy data for the first DataFrame. Line 10: We define the columns for the first DataFrame.; Line 11: We create the first spark DataFrame df_1 with the dummy data in lines 6–9 and the columns …not preserve the order of the left keys unlike pandas. on: Column or index level names to join on. These must be found in both DataFrames. If on. is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames. left_on: Column or index level names to join on in the left DataFrame.When you join two Spark DataFrames using Left Anti Join (left, left anti, left_anti), it returns only columns from the left DataFrame for non-matched records. In …{"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"resources","path":"resources","contentType":"directory"},{"name":"README.md","path":"README ...Nov 30, 2022 · The join-type. [ INNER ] Returns the rows that have matching values in both table references. The default join-type. LEFT [ OUTER ] Returns all values from the left table reference and the matched values from the right table reference, or appends NULL if there is no match. It is also referred to as a left outer join. Getting pyspark.sql.utils.ParseException: missing ')' at 'in' in pyspark sql. 0. Pyspark code error: Invalid argument, not a string or column. Hot Network Questions Where to put vibrator when transit flight goes through UAE and dubaiLeft semi joins (as in ExampleÂ 4-9 and TableÂ 4-7) and left anti joins (as in TableÂ 4-8) are the only kinds of joins that only have values from the left table. A left semi join is the same as filtering the left table for only rows with keys present in the right table. The left anti join also only returns data from the left table, but ... In the below code, we used the indicator to find the rows which are ‘Left_only’ and subset the merged dataset, and assign it to df. finally, we retrieve the part which is only in our first data frame df1. the output is antijoin of the two data frames. Python3. import pandas as pd. # anti-join. df1 = pd.DataFrame ( {.perhaps I'm totally misunderstanding things, but basically have 2 dfs, and I wan't to get all the rows in df1 that are not in df2, and I thought this is what a left anti join would do, which apparently isn't supported in pyspark v1.6?If you’re a homeowner, you may have heard about homeowners associations (HOAs) and wondered if joining one is worth it. Homeowners associations are organizations that manage, maintain, and govern residential communities.Left Anti join in Spark dataframes [duplicate] Closed 5 years ago. I have two dataframes, and I would like to retrieve only the information of one of the dataframes, which is not found in the inner join, see the picture: I have tried several ways: Inner join and filtering the rows that return at least one null, all the types of joins described ...unmatched_df = parent_df.join(increment_df, on='id', how='left_anti') For parent_df, you need a little more step than just joining. You want all data from both side with updating the overlap, in this case, you first join with outer, which is to get all records from both. Then use coalesce.5: Left Anti Join: In the resulting DataFrame df_left_anti, you will see only the columns from the left DataFrame and the rows that do not have a match in the right DataFrame. The rows from the ...The join-type. [ INNER ] Returns the rows that have matching values in both table references. The default join-type. LEFT [ OUTER ] Returns all values from the left table reference and the matched values from the right table reference, or appends NULL if there is no match. It is also referred to as a left outer join.We start with two dataframes: dfA and dfB. dfA.join (dfB, 'user', 'inner') means join just the rows where dfA and dfB have common elements on the user column. (intersection of A and B on the user column). dfA.join (dfB, 'user', 'leftanti') means construct a dataframe with elements in dfA THAT ARE NOT in dfB. Are these two correct? sql.In a FROM clause, the LATERAL keyword allows an inline view to reference columns from a table expression that precedes that inline view. A lateral join behaves more like a correlated subquery than like most JOINs. A lateral join behaves as if the server executed a loop similar to the following: for each row in left_hand_table LHT: execute right ...Complementing the other answers, for PYSPARK < 2.3.0 you would not have Column.eqNullSafe neither IS NOT DISTINCT FROM. You still can build the <=> operator with an sql expression to include it in the join, as long as you define alias for the join queries:1 Answer. You have not used string interpolation in correct place. As suggested by @Lamanus in comment section change your code as shown below. val q1 = s"select * from empDF1 where salary > $ {sal}" scala> val df = spark.sql (q1) Hi, am getting the query from a json file and assigning to a variable.Well, the opposite of a left join is simply a right join. And since a left join looks like the following: We want the following to show - remember that it has to be an anti-join as well so that we do not get any data where the two tables coincide. Or, in other words, since we have shown that the following code is a Left Anti-Join: ;WITH ...Use PySpark joins with SQL to compare, and possibly combine, data from two or more datasources based on matching field values. This is simply called 'joins' in many cases and usually the datasources are tables from a database or flat file sources, but more often than not, the data sources are becoming Kafka topics. Regardless of data …Left Anti Join using PySpark join () function Left Anti Join using SQL expression join () method is used to join two Dataframes together based on condition specified in PySpark Azure Databricks. Syntax: dataframe_name.join () Contents [ hide] 1 What is the syntax of the join () function in PySpark Azure Databricks? 2 Create a simple DataFrameThe accepted answer gives a so called LEFT JOIN IF NULL in SQL terms. If you want all the rows except the matching ones from both DataFrames, not only left. You have to add another condition to the filter, since you want to exclude all rows which are in both. In this case we use DataFrame.merge & DataFrame.query:Calling groupBy(), union(), join() and similar functions on DataFrame results in shuffling data between multiple executors and even machines and finally repartitions data into 200 partitions by default. PySpark default defines shuffling partition to 200 using spark.sql.shuffle.partitions configuration.Join DataFrames using their indexes. If we want to join using the key columns, we need to set key to be the index in both df and right. The joined DataFrame will have key as its index. Another option to join using the key columns is to use the on parameter. DataFrame.join always uses right's index but we can use any column in df.Join in Spark SQL is the functionality to join two or more datasets that are similar to the table join in SQL based databases. Spark works as the tabular form of datasets and data frames. The Spark SQL supports several types of joins such as inner join, cross join, left outer join, right outer join, full outer join, left semi-join, left anti join.Left anti join. Left anti join results in rows from only statesPopulationDF if, and only if, there is NO corresponding row in statesTaxRatesDF. Join the two datasets by the State column as follows: val joinDF = statesPopulationDF.join (statesTaxRatesDF, statesPopulationDF ("State") === statesTaxRatesDF ("State"), "leftanti")%sqlval joinDF ...better way to select all columns and join in pyspark data frames. I have two data frames in pyspark. Their schema's are below. df1 DataFrame [customer_id: int, email: string, city: string, state: string, postal_code: string, serial_number: string] df2 DataFrame [serial_number: string, model_name: string, mac_address: string] Now I want to do a ...In PySpark we can select columns using the select () function. The select () function allows us to select single or multiple columns in different formats. Syntax: dataframe_name.select ( columns_names ) Note: We are specifying our path to spark directory using the findspark.init () function in order to enable our program to find the location of ...PySpark joins are used to combine data from two or more DataFrames based on a common field between them. There are many different types of joins. The specific join type used is usually based on the business use case as well as most optimal for performance. Joins can be an expensive operation in distributed systems like Spark as it can often lead to network shuffling. Join functionality ...#Finally join two dataframe's df1 & df2 by name merged_df=df1.unionByName(df2) merged_df.show() Conclusion. In this article, you have learned with spark & PySpark examples of how to merge two DataFrames with different columns can be done by adding missing columns to the DataFrame's and finally union them using unionByName(). Happy Learning !!In PySpark, a left anti join is a join that returns only the rows from the left DataFrame that do not contain matching rows in the right one. It is similar to a left outer …In PySpark, for the problematic column, say colA, we could simply use. import pyspark.sql.functions as F df = df.select(F.col("colA").alias("colA")) prior to using df in the join. I think this should work for Scala/Java Spark too.Left Anti join in Spark dataframes [duplicate] Closed 5 years ago. I have two dataframes, and I would like to retrieve only the information of one of the dataframes, which is not found in the inner join, see the picture: I have tried several ways: Inner join and filtering the rows that return at least one null, all the types of joins described ...Create a window: from pyspark.sql.window import Window w = Window.partitionBy (df.k).orderBy (df.v) which is equivalent to. (PARTITION BY k ORDER BY v) in SQL. As a rule of thumb window definitions should always contain PARTITION BY clause otherwise Spark will move all data to a single partition. ORDER BY is required for some functions, while ...Feb 3, 2023 · In PySpark, a left anti join is a join that returns only the rows from the left DataFrame that do not contain matching rows in the right one. It is similar to a left outer join, but only the non-matching rows from the left table are returned. Use the join() function. In PySpark, the join() method joins two DataFrames on one or more columns. The ... You can use : from pyspark.sql.functions import col and df1 is the alias name. No need to define and df_lag_pre and df_unmatched already defined. Hope this will help!86 1 7. Add a comment. 2. Change the order of the tables as you are doing left join by broadcasting left table, so right table to be broadcasted (or) change the join type to right. select /*+ broadcast (small)*/ small.*. From small right outer join large select /*+ broadcast (small)*/ small.*. From large left outer join small.PySpark Joins with SQL. Use PySpark joins with SQL to compare, and possibly combine, data from two or more datasources based on matching field values. This is simply called “joins” in many cases and usually the datasources are tables from a database or flat file sources, but more often than not, the data sources are becoming Kafka topics.1 Answer. If you want to avoid both key columns in the join result and get combined result then you can pass list of key columns as an argument to join () method. If you want to retain same key columns from both dataframes then you have to rename one of the column name before doing transformation, otherwise spark will throw ambiguous column ...better way to select all columns and join in pyspark data frames. I have two data frames in pyspark. Their schema's are below. df1 DataFrame [customer_id: int, email: string, city: string, state: string, postal_code: string, serial_number: string] df2 DataFrame [serial_number: string, model_name: string, mac_address: string] Now I want to do a ...Join DataFrames using their indexes. If we want to join using the key columns, we need to set key to be the index in both df and right. The joined DataFrame will have key as its index. Another option to join using the key columns is to use the on parameter. DataFrame.join always uses right’s index but we can use any column in df.pyspark.sql.functions.expr(str: str) → pyspark.sql.column.Column [source] ¶. Parses the expression string into the column that it represents.PySpark Join is used to combine two DataFrames and by chaining these you can join multiple DataFrames; it supports all basic join type operationsUse .drop function and drop the column after joining the dataframe .drop(alloc_ns.RetailUnit). compare_num_avails_inv = avails_ns.join( alloc_ns, (F.col('avails_ns ...In this video, I discussed about left semi, left anti & self joins in PySparkLink for PySpark Playlist:https://www.youtube.com/watch?v=6MaZoOgJa84&list=PLMWa...4. Join on Column vs Merge on Column. merge () allows us to use columns in order to combine DataFrames and by default, it uses inner join. Below example by default join on the column as this is the only common column in both DataFrames. # pandas merge - inner join by Column df3=pd.merge (df1,df2)

First - what does the Join Tool do? For now, the join tool does a simple inner join with an equal sign. That's it! In particular: • R output anchor is NOT the result of a right outer join. I know the R letter can make you think this but it is not. • Similarly: L output anchor is NOT a left outer join. I know that got me at first too!. How to get ghoul v3

🎯Day 11 of #30daysofPyspark 📌One of the most asked Pyspark beginner Interview scenario question 💡 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐞 𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐔𝐬𝐞𝐫…A left anti join returns that all rows from the first dataset which do not have a match in the second dataset.. Open in app. ... PySpark is the Python library for Spark programming. Spark is a ...I am new for PySpark. I pulled a csv file using pandas. And created a temp table using registerTempTable function. from pyspark.sql import SQLContext from pyspark.sql import Row import pandas as p...Joins in PySpark | Semi & Anti Joins | Join Data Frames in PySparkIn this video, I discussed about join() function in pyspark with inner join, left join, right join and full join examples.Link for PySpark Playlist:https://w...The Spark SQL supports several types of joins such as inner join, cross join, left outer join, right outer join, full outer join, left semi-join, left anti join. Joins scenarios are implemented in Spark SQL based upon the business use case. Some of the joins require high resource and computation efficiency.pyspark v 1.6 dataframe no left anti join? 3. Is there a right_anti when joining in PySpark? 0. Joining 2 tables in pyspark, multiple conditions, left join? 1. pyspark left join only with the first record. 1. Pyspark join with mixed conditions. 5. Broadcast left table in a join. Hot Network Questions Can Wind Wall block a Cone of Cold? …SELECT * FROM table1 t1 LEFT JOIN table2 t2 ON t2.sender_id = t1.sender_id AND t2.event_date > t1.event_date WHERE t2.sender_id IS NULL Please feel free to suggest any method other than anti-join. Thanks! sql; join; google-bigquery; anti-join; Share. Follow edited Jun 3, 2022 at 14:01. realkes. asked Jun 3, 2022 at 13:45. …{"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"resources","path":"resources","contentType":"directory"},{"name":"README.md","path":"README ...Viewed 2k times. 2. I have to write a pyspark join query. My requirement is: I only have to select records which only exists in left table. SQL solution for this is : select Left.*. FROM LEFT LEFT_OUTER_JOIN RIGHT where RIGHT.column1 is NULL and Right.column2 is NULL. For me challenge is, these 2 tables are dataframe.Connect and share knowledge within a single location that is structured and easy to search. Learn more about Teams Get early access and see previews of new features.In this post , we will learn about outer join in pyspark dataframe with example . If you want to learn Inner join refer below URL . There are other types of joins like inner join, left-anti join and left semi join. What you will learn . At the end of this tutorial, you will learn Outer join in pyspark dataframe with example. Types of outer join官方写的是 Right side of the join ，翻译过来就是放在右侧的DataFrame数据。. on：用来执行对等连接的列名，可以是字符串、字符串列表或者表达式。. 如果是字符串或者字符串列表，那么两边的数据都得存在该列。. spark的横向合并不向pandas那么简单，直接横向拼接 ...pyspark.sql.DataFrame.join. ¶. Joins with another DataFrame, using the given join expression. New in version 1.3.0. a string for the join column name, a list of column names, a join expression (Column), or a list of Columns. 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 ....

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