Pyspark array example. functions import pandas_udf from pyspark.
Pyspark array example Uses the default column name pos for position, and col for elements in the array and key and value for elements in the map unless specified otherwise. functions import pandas_udf from pyspark. containsNullbool, optional whether the array can contain null (None) values. PySpark Cheat Sheet - learn PySpark and develop apps faster View on GitHub PySpark Cheat Sheet This cheat sheet will help you learn PySpark and write PySpark apps faster. How would you implement it in Spark. Everything in here is fully functional PySpark code you can run or adapt to your programs. Parameters col Column or str The name of the column or an expression that represents the array. The new Spark functions make it easy to process array columns with native Spark. We focus on common operations for manipulating, transforming, and converting arrays in DataFrames. Apr 17, 2025 · Diving Straight into Creating PySpark DataFrames with Nested Structs or Arrays Want to build a PySpark DataFrame with complex, nested structures—like employee records with contact details or project lists—and harness them for big data analytics? Creating a DataFrame with nested structs or arrays is a powerful skill for data engineers crafting ETL pipelines with Apache Spark. The explode() and explode_outer() functions are very useful for analyzing dataframe columns containing arrays or collections. It returns a new array with null elements removed. Arrays Functions in PySpark # PySpark DataFrames can contain array columns. Example 4: Usage of array function with columns of different types. Learn data transformations, string manipulation, and more in the cheat sheet. You can think of a PySpark array column in a similar way to a Python list. With array_contains, you can easily determine whether a specific element is present in an array column, providing a Sep 16, 2025 · PySpark provides robust functionality for processing large-scale data, including reading data from various file formats such as JSON. May 18, 2023 · I have a DataFrame in PySpark with a column "c1" where each row consists of an array of integers c1 1,2,3 4,5,6 7,8,9 I wish to perform an element-wise sum (i. sql. array_agg # pyspark. They can be tricky to handle, so you may want to create new rows for each element in the array, or change them to a string. arrays_zip(*cols) [source] # Array function: Returns a merged array of structs in which the N-th struct contains all N-th values of input arrays. arrays_zip # pyspark. This article will Quick reference for essential PySpark functions with examples. >>> from pyspark. JSON (JavaScript Object Notation) is a widely used format for storing and exchanging data due to its lightweight and human-readable nature. sql import functions as sf >>> df = spark. May 24, 2025 · Learn how to use PySpark explode (), explode_outer (), posexplode (), and posexplode_outer () functions to flatten arrays and maps in dataframes. Mastering the Explode 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). The PySpark array syntax isn't similar to the list comprehension syntax that's normally used in Python. This function is particularly useful when dealing with complex data structures and nested arrays. They allow computations like sum, average, count, maximum, Mar 27, 2024 · Question: In Spark & PySpark, how to get the size/length of ArrayType (array) column and also how to find the size of MapType (map/Dic) type in DataFrame, could you also please explain with an example how to filter by array/map size? Sep 5, 2025 · In this PySpark article, I will explain the usage of collect() with DataFrame example, when to avoid it, and the difference between collect() and select(). Step-by-step guide with examples. We‘ll cover simple examples through to complex use cases for unlocking the power of array data in your PySpark workflows. slice # pyspark. Examples >>> from pyspark. These essential functions include collect_list, collect_set, array_distinct, explode, pivot, and stack. Mar 27, 2024 · 1. All these array functions accept input as an array column and several other arguments based on the function. 0 Universal License. These snippets are licensed under the CC0 1. Array Handling: Uses NumPy arrays as inputs or outputs in UDFs—e. I will explain how to use these two functions in this article and learn the differences with examples. Uses the default column name col for elements in the array and key and value for elements in the map unless specified otherwise. 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. Array columns are one of the most useful column types, but they're hard for most Python programmers to grok. sort_array # pyspark. To transform a Pyspark data frame with an array into a JSON format we follow the same procedure as in the previous example and construct a Pyspark data frame with an array field and create a JSON string and then stored it in a JSON file. 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. With array_union, you can effortlessly create a Jul 23, 2025 · Output: Output Image Method 2: Using the function getItem () In this example, first, let's create a data frame that has two columns "id" and "fruits". Notice that the input dataset is very large. foreach(f) 1. Mar 23, 2024 · In PySpark, the array_compact function is used to remove null elements from an array. New Spark 3 Array Functions (exists, forall, transform, aggregate, zip_with) Spark 3 has new array functions that make working with ArrayType columns much easier. Table of Contents Apr 27, 2025 · PySpark Type System Overview PySpark provides a rich type system to maintain data structure consistency across distributed processing. explode # pyspark. String functions can be applied to Parameters col Column or str The name of the column or an expression that represents the array. Mar 27, 2024 · PySpark SQL functions lit () and typedLit () are used to add a new column to DataFrame by assigning a literal or constant value. So let‘s get started! Nov 18, 2025 · pyspark. To split the fruits array column into separate columns, we use the PySpark getItem () function along with the col () function to create a new column for each fruit element in the array. Apr 27, 2025 · This document covers techniques for working with array columns and other collection data types in PySpark. Apache Spark and its Python API PySpark allow you to easily work with complex data structures like arrays and maps in dataframes. This operation is mainly used if you wanted to manipulate accumulators, save the DataFrame results to RDBMS tables pyspark. These functions are highly useful for data manipulation and transformation in PySpark DataFrames. Example 2: Usage of array function with Column objects. The rest of this blog uses Scala PySpark Tutorial: PySpark is a powerful open-source framework built on Apache Spark, designed to simplify and accelerate large-scale data processing and analytics tasks. array_agg(col) [source] # Aggregate function: returns a list of objects with duplicates. This function can be applied to create a new boolean column or to filter rows in a DataFrame. Dec 3, 2024 · In PySpark, understanding and manipulating these types, like structs and arrays, allows you to unlock deeper insights and handle sophisticated datasets effectively. Apr 7, 2025 · So my question is, what is the recommended way to access this type of complex data using Pyspark? Working with SQL is fine, but it would be useful for me to be able to easily query nested data in Pyspark also. The documentation provides detailed explanations, examples, and usage guidelines for all PySpark functions, including from_json. All data types in PySpark inherit from the base DataType class, which is divided into simple types (like strings and numbers) and complex types (like arrays, maps, and structs). Leverage PySpark's documentation and community support If you encounter any issues or have specific questions about from_json, refer to the official PySpark documentation. In this article, we’ll explore their capabilities, syntax, and practical examples to help you use them effectively. , converting Spark arrays to NumPy for computation, then returning to Spark. This article will Jul 10, 2025 · PySpark SQL Types class is a base class of all data types in PySpark which are defined in a package pyspark. It covers a wide range of topics, including array operations. These come in handy when we need to perform operations on an array (ArrayType) column. It is widely used in data analysis, machine learning and real-time processing. g. e just regular vector additi Dec 14, 2023 · Complex types in Spark — Arrays, Maps & Structs In Apache Spark, there are some complex data types that allows storage of multiple values in a single column in a data frame. sql import SparkSession from pyspark. 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 7. Nov 3, 2023 · This comprehensive guide will walk through array_contains () usage for filtering, performance tuning, limitations, scalability, and even dive into the internals behind array matching in Spark SQL. functions module provides string functions to work with strings for manipulation and data processing. Returns Column A new column that contains the size of each array. types import ArrayType, StringType, StructField, StructType Learn PySpark Array Functions such as array (), array_contains (), sort_array (), array_size (). This tutorial will explain with examples how to use array_union, array_intersect and array_except array functions in Pyspark. DataType and are used to create DataFrame with a specific type. Common operations include checking for array containment, exploding arrays into multiple Aug 21, 2025 · In this article, I will explain how to use the array_contains() function with different examples, including single values, multiple values, NULL checks, filtering, and joins. Learn how to flatten arrays and work with nested structs in PySpark. Example 3: Single argument as list of column names. functions import col, transform, filter, zip_with from pyspark. Jul 23, 2025 · We can use the sort () function or orderBy () function to sort the Spark array, but these functions might not work if an array is of complex data type. These functions can also be used to convert JSON to a struct, map type, etc. posexplode(col) [source] # Returns a new row for each element with position in the given array or map. In this tutorial, we explored set-like operations on arrays using PySpark's built-in functions like arrays_overlap(), array_union(), flatten(), and array_distinct(). The length specifies the number of elements in the resulting array. createDataFrame( The PySpark array syntax isn't similar to the list comprehension syntax that's normally used in Python. Examples Example 1: Basic usage with integer array This tutorial will explain multiple workarounds to flatten (explode) 2 or more array columns in PySpark. This function is particularly useful when dealing with datasets that contain arrays, as it simplifies the process of merging and deduplicating them. This post covers the important PySpark array operations and highlights the pitfalls you should watch out for. Examples Example 1: Basic usage with integer array Dec 15, 2021 · In PySpark data frames, we can have columns with arrays. Mar 27, 2024 · In PySpark, the JSON functions allow you to work with JSON data within DataFrames. Oct 13, 2025 · PySpark ArrayType (Array) Functions PySpark SQL provides several Array functions to work with the ArrayType column, In this section, we will see some of the most commonly used SQL functions. This function is useful when dealing with arrays in DataFrame columns, especially when you want to clean up or filter out null values from array-type columns. I will explain the most used JSON SQL functions with Python examples in this article. posexplode # pyspark. Both these functions return Column type as return type. In this article, you will learn different Data Types and their utility methods with Python examples. Mar 21, 2024 · PySpark provides a wide range of functions to manipulate, transform, and analyze arrays efficiently. You can refer to the cookbook for practical examples and tips on using the array_intersect function effectively. This blog post will demonstrate Spark methods that return ArrayType columns, describe how to create your own ArrayType columns, and explain when to use arrays in your analyses. Jun 8, 2017 · 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. Introduction to the array_union function The array_union function in PySpark is a powerful tool that allows you to combine multiple arrays into a single array, while removing any duplicate elements. The getItem () function is a PySpark SQL function that Jul 18, 2025 · PySpark is the Python API for Apache Spark, designed for big data processing and analytics. Nov 8, 2023 · This tutorial explains how to explode an array in PySpark into rows, including an example. 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. You can use array_contains () function either to derive a new boolean column or filter the DataFrame. pyspark. Nov 5, 2021 · I can use array_contains to check whether an array contains a value. Related Articles: How to Iterate PySpark DataFrame through Loop How to Convert PySpark DataFrame Column to Python List In order to explain with an example, first, let’s create a DataFrame. For such complex data type arrays, we need to use different ways to sort an array of a complex data type in PySpark which will be defined in this article using Python. Mar 17, 2023 · Collection functions in Spark are functions that operate on a collection of data elements, such as an array or a sequence. functions module. It returns null if the array itself is null, true if the element exists, and false otherwise. These examples demonstrate filtering rows based on array values, getting distinct elements from the array, removing specific elements, and transforming each element using a lambda function. Reading JSON files into a PySpark DataFrame enables users to perform powerful data transformations, analyses, and machine Dec 14, 2023 · Complex types in Spark — Arrays, Maps & Structs In Apache Spark, there are some complex data types that allows storage of multiple values in a single column in a data frame. Sep 17, 2025 · How to check elements in the array columns of a PySpark DataFrame? PySpark provides two powerful higher-order functions, such as exists() and forall() to Dec 3, 2024 · In PySpark, understanding and manipulating these types, like structs and arrays, allows you to unlock deeper insights and handle sophisticated datasets effectively. Here's a brief explanation of… Mar 27, 2024 · PySpark SQL collect_list() and collect_set() functions are used to create an array (ArrayType) column on DataFrame by merging rows, typically after group by or window partitions. May 12, 2024 · The StructType and StructField classes in PySpark are used to specify the custom schema to the DataFrame and create complex columns like nested struct, array, and map columns. This article will explore how to work with complex data types in PySpark, including practical examples of accessing and transforming nested columns. Oct 4, 2024 · PySpark — Flatten Deeply Nested Data efficiently In this article, lets walk through the flattening of complex nested data (especially array of struct or array of array) efficiently without the … This tutorial will explain with examples how to use array_sort and array_join array functions in Pyspark. This tutorial will explain with examples how to use array_position, array_contains and array_remove array functions in Pyspark. Table of Contents Mar 27, 2024 · In PySpark, the JSON functions allow you to work with JSON data within DataFrames. . Jan 23, 2018 · I'm trying to create a schema for my new DataFrame and have tried various combinations of brackets and keywords but have been unable to figure out how to make this work. slice(x, start, length) [source] # Array function: Returns a new array column by slicing the input array column from a start index to a specific length. types. 2 PySpark foreach () Usage When foreach() applied on PySpark DataFrame, it executes a function specified in for each element of DataFrame. These Jan 1, 2025 · PySpark, a distributed data processing framework, provides robust support for complex data types like Structs, Arrays, and Maps, enabling seamless handling of these intricacies. In this comprehensive guide, we will cover how to use these functions with plenty of examples. Oct 7, 2025 · The PySpark sql. Aug 19, 2025 · In this PySpark article, you will learn how to apply a filter on DataFrame columns of string, arrays, and struct types by using single and multiple conditions and also using isin() with PySpark (Python Spark) examples. Let’s explore how to master the explode function in Spark DataFrames to unlock Transforming Arrays and Maps in PySpark This tutorial explains advanced functions in PySpark to manipulate array and map collections using: transform() filter() zip_with() Sample Data Setup from pyspark. This tutorial will explain explode, posexplode, explode_outer and posexplode_outer methods available in Pyspark to flatten (explode) array column. PySpark DataFrame foreach () 1. My current attempt: from Jul 23, 2025 · Example 2: Transforming a Pyspark DataFrame with an array into a JSON format. Let’s see an example of an array column. Introduction to array_contains function The array_contains function in PySpark is a powerful tool that allows you to check if a specified value exists within an array column. First, we will load the CSV file from S3. Here’s an example with a NumPy UDF: from pyspark. functions transforms each element of an array into a new row, effectively “flattening” the array column. This PySpark DataFrame Tutorial will help you start understanding and using PySpark DataFrame API with Python examples. Introduction […] Sep 23, 2025 · PySpark is widely adopted by Data Engineers and Big Data professionals because of its capability to process massive datasets efficiently using distributed computing. Mar 11, 2024 · Exploring Spark’s Array Data Structure: A Guide with Examples Introduction: Apache Spark, a powerful open-source distributed computing system, has become the go-to framework for big data … Aug 21, 2025 · The PySpark array_contains() function is a SQL collection function that returns a boolean value indicating if an array-type column contains a specified element. functions. Example 1: Basic usage of array function with column names. My current attempt: from Aug 7, 2025 · The explode function in PySpark is a transformation that takes a column containing arrays or maps and creates a new row for each element in the array or key-value pair in the map. All DataFrame examples provided in this Tutorial were tested in our development environment and are available at PySpark-Examples GitHub project for easy reference. For Python users, related PySpark operations are discussed at PySpark Explode Function and other blogs. StructType is a collection of StructField objects that define column name, column data type, boolean to specify if the field can be nullable or not, and metadata. Feb 25, 2024 · In PySpark, explode, posexplode, and outer explode are functions used to manipulate arrays in DataFrames. When dealing with array columns—common in semi Jul 23, 2025 · To split multiple array column data into rows Pyspark provides a function called explode (). This function applies the specified transformation on every element of the array and returns an object of ArrayType. PySpark Cookbook: The PySpark Cookbook is a community-driven collection of recipes and solutions for common PySpark tasks. Reading JSON files into a PySpark DataFrame enables users to perform powerful data transformations, analyses, and machine PySpark explode (), inline (), and struct () explained with examples. Examples Example 1: Removing null values from a simple array pyspark. 1 foreach () Syntax Following is the syntax of the foreach () function # Syntax DataFrame. Detailed tutorial with real-time examples. Mar 21, 2025 · When working with data manipulation and aggregation in PySpark, having the right functions at your disposal can greatly enhance efficiency and productivity. These data types can be confusing, especially… Dec 27, 2023 · Arrays are a critical PySpark data type for organizing related data values into single columns. transform () is used to apply the transformation on a column of type Array. ArrayType # class pyspark. ArrayType(elementType, containsNull=True) [source] # Array data type. The indices start at 1, and can be negative to index from the end of the array. 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. Returns Column A new column that contains the maximum value of each array. In this comprehensive guide, we will explore the key array features in PySpark DataFrames and how to use three essential array functions – array_union, array_intersect and array_except […] Sep 4, 2025 · Iterating over elements of an array column in a PySpark DataFrame can be done in several efficient ways, such as explode() from pyspark. This post covers the Apr 26, 2024 · Spark with Scala provides several built-in SQL standard array functions, also known as collection functions in DataFrame API. Spark developers previously needed to use UDFs to perform complicated array functions. Whether you’re preparing for a data engineering interview or working on real-world big data projects, having a strong command of PySpark functions can significantly improve your productivity and problem-solving skills. From below example column “subjects” is an array of ArraType which holds subjects learned. Using explode, we will get a new row for each element in the array. Oct 16, 2025 · PySpark MapType (also called map type) is a data type to represent Python Dictionary (dict) to store key-value pair, a MapType object comprises three fields, keyType (a DataType), valueType (a DataType) and valueContainsNull (a BooleanType). Sep 13, 2024 · If you’re working with PySpark, you’ve likely come across terms like Struct, Map, and Array. array_intersect # pyspark. sort_array(col, asc=True) [source] # Array function: Sorts the input array in ascending or descending order according to the natural ordering of the array elements. explode(col) [source] # Returns a new row for each element in the given array or map. typedLit() provides a way to be explicit about the data type of the constant value being added to a DataFrame, helping to ensure data consistency and type correctness of PySpark workflows. types import ArrayType, StringType Nov 19, 2025 · Aggregate functions in PySpark are essential for summarizing data across distributed datasets. This tutorial will explain with examples how to use arrays_overlap and arrays_zip array functions in Pyspark. These functions allow you to manipulate and transform the data in various 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. These functions help you parse, manipulate, and extract data from JSON columns or strings. These Apr 17, 2025 · How to Filter Rows with array_contains in an Array Column in a PySpark DataFrame: The Ultimate Guide Diving Straight into Filtering Rows with array_contains in a PySpark DataFrame Filtering rows in a PySpark DataFrame is a critical skill for data engineers and analysts working with Apache Spark in ETL pipelines, data cleaning, or analytics. Arrays can be useful if you have data of a variable length. Parameters col Column or str name of column or expression Returns Column A new column that is an array excluding the null values from the input column. 4 days ago · In this article, I will explain how to explode an array or list and map columns to rows using different PySpark DataFrame functions explode(), Working with PySpark ArrayType Columns This post explains how to create DataFrames with ArrayType columns and how to perform common data processing operations. array_intersect(col1, col2) [source] # Array function: returns a new array containing the intersection of elements in col1 and col2, without duplicates. If one of the arrays is shorter than others then the resulting struct type value will be a null for missing elements. Null elements will be placed at the beginning of the returned array in ascending order or at the end of the returned array in descending order. Working with Spark ArrayType columns Spark DataFrame columns support arrays, which are great for data sets that have an arbitrary length. Notes Supports Spark Connect. Before we start, let’s create a DataFrame with a nested array column. types import DoubleType import numpy as np Nov 21, 2025 · To convert a string column (StringType) to an array column (ArrayType) in PySpark, you can use the split() function from the pyspark. Parameters elementType DataType DataType of each element in the array. This allows for efficient data processing through PySpark‘s powerful built-in array manipulation functions. Learn how to manipulate arrays in PySpark using slice (), concat (), element_at (), and sequence () with real-world DataFrame examples. See this post if you're using Python / PySpark. It offers a high-level API for Python programming language, enabling seamless integration with existing Python ecosystems. Apr 9, 2024 · Spark array_contains() is an SQL Array function that is used to check if an element value is present in an array type (ArrayType) column on DataFrame. It lets Python developers use Spark's powerful distributed computing to efficiently process large datasets across clusters. sbkmwqdxswaeyxokzxprmkbfxqdxmfcdxicbxasqvagojqqviqdcksyvccgyodavlrwka