Spark explode multiple columns First you could create a table with just 2 columns, the 2 letter encoding and the rest of the content in another column. functions. I am not familiar with the map reduce concept to change the script here to pyspark myself. Sample DF: from pyspark import Row from pyspark. Apr 24, 2024 · In this article, I will explain how to explode array or list and map DataFrame columns to rows using different Spark explode functions (explode, The explode functions are built-in Spark SQL functions designed to convert array columns into multiple rows. It is widely used when working with nested JSON or complex data types. Example 1: Parse a Column of JSON Strings Using pyspark. functions import explode sqlc = SQLContext( Jun 9, 2024 · Splitting Multiple Array Columns into Rows To split multiple array columns into rows, we can use the PySpark function “explode”. Splitting the struct column into separate columns makes it easier to access and manipulate the data. Jul 23, 2025 · There are a few reasons why we might want to split a struct column into multiple columns in a DataFrame: Ease of Use: Struct columns can be difficult to work with, especially when we need to access individual fields within the struct. sql. Various variants of explode help handle special cases like NULL values or when position What is the PySpark Explode Function? The PySpark explode function is a transformation operation in the DataFrame API that flattens array-type or nested columns by generating a new row for each element in the array, managed through SparkSession. pyspark. Aug 15, 2025 · If you have multiple columns, it’s not good to hardcode map key names, let’s see the same by programmatically. So I'm going to start here by showing the data. It is better to explode them separately and take distinct values each time. from pyspark. See full list on sparkbyexamples. 2. In this video, we dive into the powerful capabilities of Spark SQL, focusing on the technique of exploding multiple columns within your datasets. In this method, we will see how we can convert a column of type 'map' to multiple columns in a data frame using explode function. column. Feb 19, 2025 · To explode a single column in a Polars DataFrame, you can use the explode() method, specifying the column you want to explode. functions import explode #explode points column into rows df_new = df. withColumn('points', explode(df. This article shows you how to flatten or explode a * StructType *column to multiple columns using Spark SQL. add two additional columns to the dataframe called "id" and "name")? The methods aren't exactly the same, and I can only figure out how to create a brand new data frame using: Nov 20, 2024 · Learn the syntax of the explode function of the SQL language in Databricks SQL and Databricks Runtime. Here we will parse or read json string present in a csv file and convert it into multiple dataframe columns using Python Pyspark. explode(col: ColumnOrName) → pyspark. com Jul 23, 2025 · Working with the array is sometimes difficult and to remove the difficulty we wanted to split those array data into rows. Name age subject parts xxxx 21 Maths,Physics I yyyy 22 English,French I,II I am trying to explode the above dataframe in both su Nov 29, 2017 · Assuming you are using Spark 2. from_json For parsing json string we'll use from_json () SQL function to parse the Mar 27, 2018 · I have a spark dataframe looks like: id DataArray a array(3,2,1) b array(4,2,1) c array(8,6,1) d array(8,2,4) I want to transform this dataframe into: id col1 col2 col3 a Oct 5, 2020 · explode column with comma separated string in Spark SQL Asked 5 years, 1 month ago Modified 4 years, 4 months ago Viewed 10k times. points)) This particular example explodes the arrays in the points column of a DataFrame into multiple rows. This function converts the list elements to a row while replacing the index values and returning the DataFrame exploded list. We often need to flatten such data for easier analysis. 1. Column ¶ Returns a new row for each element in the given array or map. e. *, as shown below: Nov 20, 2024 · Learn the syntax of the explode function of the SQL language in Databricks SQL and Databricks Runtime. Each element in the array or map becomes a separate row in the resulting DataFrame. Jul 23, 2025 · Output: Method 3: Using explode () function The function that is used to explode or create array or map columns to rows is known as explode () function. functions import explode,map_keys,col I found the answer in this link How to explode StructType to rows from json dataframe in Spark rather than to columns but that is scala spark and not pyspark. Oct 13, 2025 · Use explode_outer() to retain rows even when arrays or maps are null or empty. The explode function in Spark DataFrames transforms columns containing arrays or maps into multiple rows, generating one row per element while duplicating the other columns in the DataFrame. Apr 27, 2025 · Sources: All files Summary Explode and flatten operations are essential tools for working with complex, nested data structures in PySpark: Explode functions transform arrays or maps into multiple rows, making nested data easier to analyze. Uses the default column name col for elements in the array and key and value for elements in the map unless specified otherwise. functions), explode takes a column containing arrays—e. The main query then joins the original table to the CTE on id so we can combine original simple columns with exploded simple columns from the nested array. explode ¶ pyspark. *, as shown below: Oct 29, 2021 · I have the below spark dataframe. PySpark: Dataframe Explode Explode function can be used to flatten array column values into rows in Pyspark. I used @MaFF's solution first for my problem but that seemed to cause a lot of errors and additional computation time. Jul 29, 2017 · There was a question regarding this issue here: Explode (transpose?) multiple columns in Spark SQL table Suppose that we have extra columns as below: **userId someString varA varB Aug 21, 2017 · I needed to unlist a 712 dimensional array into columns in order to write it to csv. explode(col) [source] # Returns a new row for each element in the given array or map. Sep 28, 2021 · The approach uses explode to expand the list of string elements in array_column before splitting each string element using : into two different columns col_name and col_val respectively. Jun 13, 2021 · And I would like to explode multiple columns at once, keeping the old column names in a new column, such as: Jan 19, 2025 · Enter Spark’s explode function—a simple yet powerful tool that can make your life much easier when dealing with nested columns. x, I think what you are looking for is the pivot operation on the spark dataframe. , lists, JSON arrays—and Jul 9, 2022 · In Spark, we can create user defined functions to convert a column to a StructType. . Split Multiple Array Columns into Rows To split multiple array column data into rows Pyspark provides a function called explode (). Examples Sometimes your PySpark DataFrame will contain array-typed columns. This article was written with Scala 2. Why do we need these functions? All four functions share the same core purpose: they take each element inside 33 I am using Spark SQL (I mention that it is in Spark in case that affects the SQL syntax - I'm not familiar enough to be sure yet) and I have a table that I am trying to re-structure, but I'm getting stuck trying to transpose multiple columns at the same time. g. Basically I have data that looks like: 33 I am using Spark SQL (I mention that it is in Spark in case that affects the SQL syntax - I'm not familiar enough to be sure yet) and I have a table that I am trying to re-structure, but I'm getting stuck trying to transpose multiple columns at the same time. I am not sure what was causing it, but I used a different method which reduced the computation time considerably (22 minutes compared to more than 4 hours)! Method by @MaFF's: Sep 1, 2016 · How would I do something similar with the department column (i. I am attaching a sample dataframe in similar schema and structure below. PySpark explode () Function The PySpark explode() function creates a new row for each element in an array or map column. Spark offers two powerful functions to help with this: explode() and posexplode(). May 24, 2025 · In this post, we’ll cover everything you need to know about four important PySpark functions: explode(), explode_outer(), posexplode(), and posexplode_outer(). 12 and Spark 3. These functions help you convert array or map columns into multiple rows, which is essential when working with nested data. Before we start, let’s create a DataFrame with a nested array column. What Does the explode Function Do? The explode function in Spark is designed to transform an array or map column into multiple rows, effectively “flattening” the nested structure. This will flatten the lists in the specified column, creating multiple rows where each list element gets its own row. Understanding their syntax and parameters is key to using them effectively. Nov 29, 2024 · By using Pandas DataFrame explode() function you can transform or modify each element of a list-like to a row (single or multiple columns), replicating the index values. Learn how to work with complex nested data in Apache Spark using explode functions to flatten arrays and structs with beginner-friendly examples. Flatten function combines nested arrays into a single, flat array. I need to unpack the array values into rows so I can list the distinct values. Dec 23, 2022 · I have a table where the array column (cities) contains multiple arrays and some have multiple duplicate values. , arrays or maps) and want to flatten them for analysis or processing. Jan 17, 2022 · Spark Scala - How to explode a column into multiple rows in spark scala Asked 3 years, 10 months ago Modified 3 years, 10 months ago Viewed 6k times The column holding the array of multiple records is exploded into multiple rows by using the LATERAL VIEW clause with the explode () function. Refer official documentation here. The “explode” function takes an array column as input and returns a new row for each element in the array. Whether you're working with complex data Aug 15, 2023 · Apache Spark built-in function that takes input as an column object (array or map type) and returns a new row for each element in the given array or map type column. This is particularly useful when you have nested data structures (e. What we will do is store column names of the data frame in a new data frame column by using explode () function Jul 16, 2019 · I have a dataframe (with more rows and columns) as shown below. I've tried mapping an explode accross all columns in the dataframe, but that doesn't seem to work either: Jun 28, 2018 · Pyspark: explode json in column to multiple columns Asked 7 years, 5 months ago Modified 8 months ago Viewed 88k times Oct 27, 2022 · How can I explode multiple array columns with variable lengths and potential nulls? My input data looks like this: Nov 8, 2023 · You can use the following syntax to explode a column that contains arrays in a PySpark DataFrame into multiple rows: from pyspark. Fortunately, PySpark provides two handy functions – explode() and explode_outer() – to convert array columns into expanded rows to make your life easier! Jul 23, 2025 · In this article, we are going to discuss how to parse a column of json strings into their own separate columns. This tutorial will explain following explode methods available in Pyspark to flatten (explode) array column, click on item in the below list and it will take you to the respective section of the page: explode posexplode explode_outer posexplode_outer explode & posexplode functions will Spark: explode function The explode() function in Spark is used to transform an array or map column into multiple rows. The column holding the array of multiple records is exploded into multiple rows by using the LATERAL VIEW clause with the explode () function. From below example column “subjects” is an array of ArraType which holds subjects learned. Jul 14, 2025 · Nested structures like arrays and maps are common in data analytics and when working with API requests or responses. In my dataframe, exploding each column basically just does a useless cross join resulting in dozens of invalid rows. How can I explode multiple columns pairs into multiple rows? I have a dataframe with the following client, type, address, type_2, address_2 abc, home, 123 Street, business, 456 Street I w Mar 27, 2024 · I have a Spark DataFrame with StructType and would like to convert it to Columns, could you please explain how to do it? Converting Struct type to columns May 24, 2022 · Spark essentials — explode and explode_outer in Scala tl;dr: Turn an array of data in one row to multiple rows of non-array data. You can use multiple explode() functions to expand multiple columns. This tutorial will explain multiple workarounds to flatten (explode) 2 or more array columns in PySpark. I understand how to explode a single column of an array, but I have multiple array columns where the arrays line up with each other in terms of index-values. Using explode, we will get a new row for each element in the array. Introduced as part of PySpark’s SQL functions (pyspark. explode # pyspark. sql import SQLContext from pyspark. Operating on these array columns can be challenging. Jun 8, 2017 · I have a dataset in the following way: FieldA FieldB ArrayField 1 A {1,2,3} 2 B {3,5} I would like to explode the data on ArrayField so the output will look i Oct 13, 2025 · Solution: PySpark explode function can be used to explode an Array of Array (nested Array) ArrayType(ArrayType(StringType)) columns to rows on PySpark DataFrame using python example. Sep 3, 2018 · 3 You can first make all columns struct -type by explode -ing any Array(struct) columns into struct columns via foldLeft, then use map to interpolate each of the struct column names into col. Jun 28, 2018 · When Exploding multiple columns, the above solution comes in handy only when the length of array is same, but if they are not. Basically I have data that looks like: What I want is - for each column, take the nth element of the array in that column and add that to a new row.