Spark dataframe explode. Parameters columnstr or tuple Column to explode.
Spark dataframe explode. TableValuedFunction. I think you need to work with Column objects. 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. Returns DataFrame Exploded lists to rows of the subset columns; index will be PySpark: Dataframe Explode Explode function can be used to flatten array column values into rows in Pyspark. PySpark Explode Function: A Deep Dive PySpark’s DataFrame API is a powerhouse for structured data processing, offering versatile tools to handle complex data structures in a distributed environment—all orchestrated through SparkSession. How do I do explode on a column in a DataFrame? Here is an example with som Apache Spark and its Python API PySpark allow you to easily work with complex data structures like arrays and maps in dataframes. when I print schema for the data frame - df. Jul 23, 2025 · In this article, we are going to learn how to split the struct column into two columns using PySpark in Python. Oct 4, 2024 · For array of array kind of columns, returns a list of such column names 2) A function flatten_without_explode which takes dataframe as input and performs below steps: Jun 18, 2024 · Use the explode function if you need to transform array or dictionary data fields in a dataframe into their constituent parts and put them in separate records in a dataframe. Example 3: Exploding multiple array columns. ignore_indexbool, default False If True, the resulting index will be labeled 0, 1, …, n - 1. 11. TableValuedFunction. alias (): Renames a column. Returns DataFrame Apr 28, 2025 · A column with comma-separated list Imagine we have a Spark DataFrame with a column called "items" that contains a list of items separated by commas. For Python users, related PySpark operations are discussed at PySpark DataFrame String Manipulation and other blogs. I'll walk you through the steps with a real-world The explode function in SparkSQL is a powerful tool for transforming a DataFrame by creating a new row for each element in the given array or map. Example 2: Exploding a map column. Jul 23, 2025 · In this article, we are going to discuss how to parse a column of json strings into their own separate columns. Use this to aggregate items from individual dataframe records into collections. This can be particularly useful when you have a DataFrame with a column containing lists or arrays and you want to expand these lists into individual rows. explode_outer(col) [source] # Returns a new row for each element in the given array or map. Uses the default column name col for elements in the array and key and value for elements in the map unless specified otherwise. Jun 15, 2021 · I tried the explode function, but the following code just returns the same data frame as above with just the headers changed. ID input_array 1 [ {“A”:300, “B”:400}, { “A”:500,”B”: 600 Oct 10, 2025 · Problem: How to explode Array of StructType DataFrame columns to rows using Spark. Generalize for Deeper Nested Structures For deeply nested JSON structures, you can apply this process recursively by continuing to use select, alias, and explode to flatten additional layers. I think it is possible with RDD's with flatmap - and, help is greatly appreciated. how to explode a spark dataframe Asked 5 years, 8 months ago Modified 5 years, 8 months ago Viewed 717 times This tutorial will explain multiple workarounds to flatten (explode) 2 or more array columns in PySpark. functions provides a function split() to split DataFrame string Column into multiple columns. This converts it to a DataFrame. val signals: DataFrame = spark. Returns a new row for each element in the given array or map. createDataset. This operation flattens the data, creating a long format DataFrame where each item from the list (or array) becomes an individual row. functions import explode # create a sample DataFrame df = spark. Each row of the resulting DataFrame will contain one element of the original array column Sep 28, 2016 · I have a Dataframe that I am trying to flatten. Each element in the list or array is placed in its own row, with the values in the other columns being repeated accordingly. For example, if our dataframe had a list of nulls instead of a null list the result would Sep 29, 2023 · Is there a way to explode a Struct column in a Spark DataFrame like you would explode an Array column? Meaning to take each element of the Struct (a key-value pair) value and create a separate row for each. " sounds like OP is stating a fact, rather than what they have tried. I have one of the data types which is of type struct within keys. explode(column, ignore_index=False) [source] # Transform each element of a list-like to a row, replicating index values. Oct 12, 2018 · @user6910411 Thank you for your comment, but when I use explode (), the length of my dataframe all_items becomes approximately (length_of array_in_row)x (number_of_rows). createDataDrame () method, which takes the data as one of its parameters. explode explode function creates a new row for each element in the given array or map column (in a DataFrame). This is particularly useful when you have nested data structures (e. withColumn("name", explodeDeptDF("department. I did realize shortly after my post that there is actually a DataFrame solution using collect_set (or collect_list Jul 23, 2025 · After that create a DataFrame using the spark. pyspark. In this comprehensive guide, we will cover how to use these functions with plenty of examples. Example 1: Parse a Column of JSON Strings Using pyspark. Step-by-step guide with examples. See full list on sparkbyexamples. This article shows you how to flatten or explode a * StructType *column to multiple columns using Spark SQL. explode_outer # pyspark. in the lambda function, you will match the input by case. functions import explode #explode points column into rows df_new = df. Explode can be used to convert one row into multiple rows in Spark. Here's an example case class Apr 30, 2021 · In this How To article I will show a simple example of how to use the explode function from the SparkSQL API to unravel multi-valued fields. Feb 27, 2024 · To flatten (explode) a JSON file into a data table using PySpark, you can use the explode function along with the select and alias functions. id")) explodeDeptDF = explodeDeptDF. withColumn('points', explode(df. Jul 23, 2025 · Using explode, we will get a new row for each element in the array. This blog talks through how using explode() in PySpark can help to transform JSON data into a PySpark DataFrame which takes advantage of Spark clusters to increase processing speeds whilst managing your nested properties. , arrays or maps) and want to flatten them for analysis or processing. Sometimes your PySpark DataFrame will contain array-typed columns. *" is used to tranform a struct column into columns of fields of that struxt Feb 25, 2025 · Combining these two parts, the code creates a DataFrame from the JSON data stored in the data variable, enabling you to perform various transformations and actions using Spark DataFrame API. 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 I am new to Python a Spark, currently working through this tutorial on Spark's explode operation for array/map fields of a DataFrame. Apr 29, 2023 · To iterate over the elements of an array column in a PySpark DataFrame: from pyspark. the printSchema () and show () methods are used to display the schema and the dataframe as the output. Returns an encrypted value of inputusing AES in given modewith the specified padding. functions module. The explode() family of functions converts array elements or map entries into separate rows, while the flatten() function converts nested arrays into single-level arrays. g. May 27, 2017 · If I understand your question, you can use from_json on JSON string to get table (dataframe) out of it. 1 DataFrame explode list of JSON objects edited May 23, 2017 at 12:10 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 in the following way: FieldA FieldB ExplodedField 1 A 1 1 A 2 1 A 3 2 B 3 2 B 5 I mean I want to generate an output line for each item in the array the in ArrayField while keeping the values of the other fields. explode(collection)[source] # Returns a DataFrame containing a new row for each element in the given array or map. explode() method is used to transform columns containing lists or arrays into separate rows. pandas. functions import explode, col, I've seen various people suggesting that Dataframe. Since my arrays hold about 10,000 items and there are 20 mln rows, it creates a dataframe with 10,000 x 20 mln rows and that's too big to work with. With the SparkSession established, sample data represented as a list of tuples is transformed into a DataFrame with specified column names. withColumn("element", explode($"data. createDataFrame([(1, ["apple", "banana 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 Aug 10, 2019 · Pivoting and unpivoting are very commonly-used data transformation operations. As part of the process, I want to explode it, so if I have a column of arrays, each value of the array will be used to create a separate row. For ins Jul 10, 2023 · In the world of big data, PySpark has emerged as a powerful tool for data processing and analysis. 8, spark 2. Array columns, which store collections of values like lists of tags, emails, or log entries Jul 31, 2018 · I've got an output from Spark Aggregator which is List[Character] case class Character(name: String, secondName: String, faculty: String) val charColumn = HPAggregator. I have an ud May 24, 2022 · Common gotcha with explode Note that I said explode will filter out null source columns, not null values. explode (): Converts an array into multiple rows, one for each element in the array. Its Python API enables you to manipulate distributed data using familiar pandas-like DataFrame constructs. Fortunately, PySpark provides two Oct 12, 2024 · Key Functions Used: col (): Accesses columns of the DataFrame. Let’s explore how to master the split function in Spark DataFrames Databricks Spark SQL Explode Array to Rows with Lateral View 10th October 2021 When working with JSON source files in Databricks, it's common to load that data into DataFrames with nested arrays. 0. Jun 25, 2025 · The DataFrame API for Table-Valued Functions offers a unified and intuitive way to perform data transformations in Spark with SQL, DataFrame, and Python UDTF. 1 The explode function is very slow - so, looking for an alternate method. Also, if it were a MapType() it would not display as shown in the post. read. explode ¶ DataFrame. functions. Learn the syntax of the explode function of the SQL language in Databricks SQL and Databricks Runtime. Apr 15, 2023 · When we perform a "explode" function into a dataframe we are focusing on a particular column, but in this dataframe there are always other columns and they relate to each other, so after the Apr 24, 2017 · 12 spark. The collect_list function can be thought of as the inverse of the explode function. Example 1: Exploding an array column. Solution: Spark explode function can be used to explode an Array of Jan 30, 2025 · A Deep Dive into flatten vs explode A short article on flatten, explode, explode outer in PySpark In my previous article, I briefly mentioned the explode function but didn’t get the chance to pyspark. Mine differs because my second column is an "array of structs". May 16, 2018 · Scala 2. I have a dataframe which has one row, and several columns. Oct 6, 2020 · I have a dataframe import os, sys import json, time, random, string, requests import pyodbc from pyspark import SparkConf, SparkContext, SQLContext from pyspark. Spark is an open-source, distributed processing system that is widely used for big data workloads. Refer official documentation here. One of the most common tasks data scientists encounter is manipulating data structures to fit their needs. This built-in function is available in pyspark. Introduction […] Apr 27, 2025 · Explode and Flatten Operations Relevant source files Purpose and Scope This document explains the PySpark functions used to transform complex nested data structures (arrays and maps) into more accessible formats. 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. points)) This particular example explodes the arrays in the points column of a DataFrame into multiple rows. How would you implement it I would like to transform from a DataFrame that contains lists of words into a DataFrame with each word in its own row. 上述示例中，我们创建了一个包含学生信息的 DataFrame，其中的 “grades” 列是一个嵌套的结构，包含了每个学生的成绩信息。通过使用 explode 方法，我们首先拆解了嵌套结构的列，然后提取出了每个学生的每门课的分数信息。最后，我们打印出提取后的 DataFrame，可以看到我们成功地从复杂结构中 . Jul 23, 2025 · UDF of MapType with mixed value type To process data using Spark, essential modules are imported, enabling the creation of a SparkSession, definition of UDFs, column manipulation, and data type specification. Some of the columns are single values, and others are lists. explode creates new rows -- he wants to add columns. When an array is passed to this function, it creates a new default column, and it contains all array elements as its rows, and the null values present in the array will be ignored. In this example, we are taking a list of tuples as the dataset. In Spark, we can use “explode” method to convert single column values into multiple rows. Jan 30, 2024 · Splitting nested data structures is a common task in data analysis, and PySpark offers two powerful functions for handling arrays: explode() and explode_outer(). Using split () function Mar 7, 2024 · Flattening multi-nested JSON columns in Spark involves utilizing a combination of functions like json_regexp_extract, explode, and… Jan 1, 2018 · One way may be to create one dataframe of dates to join with like @Volodymyr suggested using this method. Here's a brief explanation of… 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, Sep 1, 2016 · You could use something like that: var explodeDF = explodeDF. This is particularly useful when working with nested data structures and can help in normalizing the data for analysis. Dec 13, 2021 · I have a Spark Dataframe with the following contents: Name E1 E2 E3 abc 4 5 6 I need the various E columns to become rows in a new column as shown below: Name value EType abc 4 E1 abc 5 E2 abc 6 E3 Feb 7, 2023 · The first method uses PySpark functions such as “sequence”, “explode”, and “cast” to create the DataFrame, while the second method uses the Pandas library to generate a range of dates and then convert them to Python datetime objects before creating a PySpark DataFrame. Example 4: Exploding an array of struct column. What Mar 16, 2023 · explode () is a function that is used to transform a column of array into multiple rows ". How can I change the code to get the expected output? Jun 7, 2020 · I'm new to Scala/Spark and I'm trying to make explode a dataframe that has an array column and array of struct column so that I end up with no arrays and no struct. explode(column: Union [Any, Tuple [Any, …]], ignore_index: bool = False) → pyspark. Let’s explore how to master pivoting and unpivoting in Spark DataFrames to pyspark. In this blog post, we'll explore how to change a PySpark DataFrame column from string to array before using the explode function. Jan 17, 2022 · Spark Scala - How to explode a column into multiple rows in spark scala Asked 3 years, 9 months ago Modified 3 years, 9 months ago Viewed 6k times Apr 26, 2016 · This works for me but with one exception. Parameters collectionColumnTarget column to work on. Don't think explode is going to do it. I want to split each list column into a Jan 30, 2020 · In this post, I’ll share my experience with Spark function explode and one case where I’m happy that I avoided using it and created a faster approach to a particular use case. Among these tools, the explode function stands out as a key utility for flattening nested or array-type data, transforming it into individual rows for 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. These Dec 29, 2023 · PySpark ‘explode’ : Mastering JSON Column Transformation” (DataBricks/Synapse) “Picture this: you’re exploring a DataFrame and stumble upon a column bursting with JSON or array-like … Feb 19, 2025 · In Polars, the DataFrame. Here we will parse or read json string present in a csv file and convert it into multiple dataframe columns using Python Pyspark. May 24, 2025 · Learn how to use PySpark explode (), explode_outer (), posexplode (), and posexplode_outer () functions to flatten arrays and maps in dataframes. In this tutorial, you will learn how to split Dataframe single column into multiple columns using withColumn() and select() and also will explain how to use regular expression (regex) on split function. DataFrame ¶ Transform each element of a list-like to a row, replicating index values. Aug 24, 2016 · explode of a dataframe still return a dataframe. Disclaimer: I Sep 8, 2020 · How can we explode multiple array column in Spark? I have a dataframe with 5 stringified array columns and I want to explode on all 5 columns. Essentially select the min booking date and the max arrival date, compute the difference in days, and create one dataframe with all dates inbetween. withColumn("id", explodeDF("department. sql. explode(collection) [source] # Returns a DataFrame containing a new row for each element in the given array or map. Please do not hesitate to I have a column 'true_recoms' in spark dataframe: -RECORD 17----------------------------------------------------------------- item | 20380109 Mar 22, 2023 · TL;DR Having a document based format such as JSON may require a few extra steps to pivoting into tabular format. One such function is explode, which is particularly… Converting Array Columns into Multiple Rows in Spark DataFrames: A Comprehensive Guide Apache Spark’s DataFrame API is a robust framework for processing large-scale datasets, offering a structured and distributed environment for executing complex data transformations with efficiency and scalability. Apr 25, 2023 · The explode function in PySpark is used to transform a column with an array of values into multiple rows. DataFrame. Based on the very first section 1 (PySpark explode array or map Feb 25, 2024 · In PySpark, explode, posexplode, and outer explode are functions used to manipulate arrays in DataFrames. Operating on these array columns can be challenging. explode is a useful way to do this, but it results in more rows than the original dataframe, which isn't what I want at all. Showing example with 3 columns for the sake of simplic Aug 26, 2025 · How to Create a PySpark DataFrame with a Timestamp Column for a Date Range? You can use several built-in PySpark SQL functions like sequence(), explode(), and to_date() to create a PySpark DataFrame with a timestamp column. tvf. To extract the individual items from this column, we can use the split () function. frame. It is designed to be fast, easy to use, and flexible, and it provides a wide range of functionality for data processing, including data transformation, aggregation, and analysis. 4. select (*exprs), it returns all the data types to string. Jul 9, 2022 · In Spark, we can create user defined functions to convert a column to a StructType. For Python users, related PySpark operations are discussed at PySpark DataFrame Pivot and other blogs. I have found this to be a pretty common use case when doing data cleaning using PySpark, particularly when working with nested JSON documents in an Extract Transform and Load workflow. Sep 26, 2020 · Meanwhile, assuming that df is the dataframe being used, what we need to do, is to create a new dataframe, while exrtracting the vals from the previous property array to new columns, and droping the property column at last : Check how to explode arrays in Spark and how to keep the index position of each element in SQL and Scala with examples. The explode() and explode_outer() functions are very useful for analyzing dataframe columns containing arrays or collections. Only one explode is allowed per SELECT clause. Thanks to @titipat for giving the RDD solution. All list columns are the same length. Dec 1, 2023 · Mastering the Split Function in Spark DataFrames: A Comprehensive Guide This tutorial assumes you’re familiar with Spark basics, such as creating a SparkSession and working with DataFrames (Spark Tutorial). toColumn val resultDF = someDF. name")) which you helped me into and these questions: Flattening Rows in Spark Spark 1. And it accept a lambda function f: (Row) ⇒ TraversableOnce [A] as parameter. The JSON reader infers the schema automatically from the JSON string. Unlike explode, if the array/map is null or empty then null is produced. explode # TableValuedFunction. asTable returns a table argument in PySpark. 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 Nov 18, 2022 · Explode dates and backfill rows in pyspark dataframe Asked 2 years, 11 months ago Modified 2 years, 11 months ago Viewed 824 times Jun 13, 2018 · Every row in the dataframe contains a csv formatted string line plus another simple string, so what I'm trying to get at the end is a dataframe composed of the fields extracted from the line string Table Argument # DataFrame. Jul 31, 2024 · In Pandas, the explode() method is used to transform each element of a list-like column into a separate row, replicating the index values for other columns. The explode() function in Spark is used to transform an array or map column into multiple rows. Oct 5, 2022 · I have a dataframe which has 2 columns" "ID" and "input_array" (values are JSON arrays). New in version 4. When using Spark with Java, you can leverage the DataFrame API to apply the explode function seamlessly. Oct 13, 2025 · Problem: How to explode & flatten nested array (Array of Array) DataFrame columns into rows using PySpark. Each element in the array or map becomes a separate row in the resulting DataFrame. Parameters columnstr or tuple Column to explode. This class provides methods to specify partitioning, ordering, and single-partition constraints when passing a DataFrame as a table argument to TVF (Table-Valued Function)s including UDTF (User-Defined Table Function)s. Feb 20, 2017 · That question has a simpler dataframe where the second column is just an array. May 20, 2022 · ] } """ Convert to DataFrame Add the JSON string as a collection type and pass it as an input to spark. This function converts the list elements to a row while replacing the index values and returning the DataFrame exploded list. datapayload")) explode creates a Column. This sample code uses a list collection type, which is represented as json :: Nil. Sep 25, 2025 · pyspark. Jun 28, 2018 · "The explode function explodes the dataframe into multiple rows. Pivoting and Unpivoting Rows in Spark DataFrames: A Comprehensive Guide This tutorial assumes you’re familiar with Spark basics, such as creating a SparkSession and working with DataFrames (Spark Tutorial). explode # DataFrame. This article delves into their Mar 13, 2025 · Apache Spark provides powerful built-in functions for handling complex data structures. 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. json(signalsJson) signals. Use them when you want to switch from a row-based to a column-based view and vice-versa. com Jun 28, 2018 · I've used the very elegant solution from @Nasty but if you have a lot of columns to explode, the scheduler on server side might run into issues if you generate lots of new dataframes with "withColumn ()". from_json For parsing json string we'll use from_json () SQL function to parse the Combining rows into an array in pyspark Yeah, I know how to explode in Spark, but what is the opposite and how do I do it? HINT (collect_list) PySpark has become a hugely popular platform for large-scale data processing due to its ability to handle immense datasets efficiently. hc2djg7 bwq 2dfkxuw szryf8 5c1m8 1vj uhd2i7 caj3v bpyw zit