: Pyspark map column mapKeyDedupPolicy. functions. Dec 22, 2022 · I have a PySpark DataFrame and I want to map values of a column. Examples Example 1: Extracting values from a simple map Jul 23, 2025 · Steps to get Keys and Values from the Map Type column in SQL DataFrame The described example is written in Python to get keys and values from the Map Type column in the SQL dataframe. functions import explode keys = [ x['key'] for x in (df. Examples Example 1: Extracting keys from a simple map pyspark. May 16, 2024 · To convert DataFrame columns to a MapType (dictionary) column in PySpark, you can use the create_map function from the pyspark. map_from_entries # pyspark. This blog post explains how to convert a map into multiple columns. pyspark. Returns a new DataFrame by adding multiple columns or replacing the existing columns that have the same names. If your columns are too many to enumerate, you could also just add a tuple to the existing row. Apr 28, 2025 · An RDD transformation that applies the transformation function to every element of the data frame is known as a map in Pyspark. This function allows you to create a map from a set of key-value pairs, where the keys and values are columns from the DataFrame. Examples >>> from pyspark. The first field of each entry is used as the key and the second field as the value in the resulting map column Essentially you have to map the row to a tuple containing all of the existing columns and add in the new column (s). I used reduce(add, ) because create_map expects the inputs to be key-value pairs in order- I couldn't Parameters col1 Column or str Name of column containing a set of keys. Examples Example 1: Basic usage of map_concat I have a pyspark dataframe consisting of one column, called json, where each row is a unicode string of json. sql import SparkSession from pyspark. So, now it would be easier to “ Display ” the Parameters text Column or str Input column or strings. That is not the case for a StructType column- in a struct column, all of the rows have the same struct fields. functions module. Continue reading the article further to Apr 26, 2016 · PySpark converting a column of type 'map' to multiple columns in a dataframe Asked 9 years, 7 months ago Modified 3 years, 4 months ago Viewed 40k times Aug 15, 2025 · PySpark DataFrame MapType is used to store Python Dictionary (Dict) object, so you can convert MapType (map) column to Multiple columns ( separate DataFrame column for every key-value). 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. Two key functions in PySpark for working with map data structures are map_keys () and map_values (). types. functions import col,lit,create_map Step 2: Now, we create a spark session using getOrCreate () function. functions import explode_outer 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. Jul 17, 2023 · Using “explode ()” Method on “Maps” It is possible to “ Create ” “ Two New Additional Columns ”, called “ key ” and “ value ”, for “ Each Key-Value Pair ” of a “ Given Map Column ” in “ Each Row ” of a “ DataFrame ” using the “ explode () ” Method form the “ pyspark. Im using python/spark 2. I'm using Python and Spark 1. Jun 26, 2016 · I'm trying to transpose some columns of my table to row. PySpark is a powerful open-source library that allows developers to use Python for big data processing. create_map(*cols) [source] # Map function: Creates a new map column from an even number of input columns or column references. Notes Keys in a map data type are not allowed to be null (None). size and for Dec 27, 2023 · PySpark is a powerful tool for large-scale data processing using Apache Spark. distinct() . . For instance, the input (key1, value1, key2, value2, …) would produce a map that associates key1 with value1, key2 with value2, and so on. Understanding these functions will help you efficiently process and analyze large Apr 30, 2015 · 2 Use the provided split and explode functions in the DataFrame API to split the data on ",". apache. Can use methods of Column, functions defined in pyspark. Oct 21, 2021 · I was trying to map the values to a new column in my pyspark df Parameters col Column or str The name of the column or a column expression representing the map to be filtered. This article will explain how the map Jul 23, 2025 · In this article, we are going to learn about converting a column of type 'map' to multiple columns in a data frame using Pyspark in Python. Is there a way to convert "col1" data into string datatype so that I can proceed with writing to a CSV file? Oct 11, 2018 · The difference between the MapType and the StructType is that the key-value pairs for the maps are row-wise independent. create_map needs a list of column expressions that are grouped as key-value pairs. The colsMap is a map of column name and column, the column must only refer to attributes supplied by this Dataset. In the below example, we replace the string value of the state column with the full abbreviated name from a dictionary key-value pair, in order to do so I use PySpark map () transformation to loop through each row of DataFrame. create_map(*cols: Union [ColumnOrName, List [ColumnOrName_], Tuple [ColumnOrName_, …]]) → pyspark. Here is my initial table: Learn how to use the map function in PySpark. A data type that represents Python Dictionary to store key-value pair, a MapType object and comprises three fields, keyType, valueType, and valueContainsNull is called map type in Pyspark. The Jul 23, 2025 · Methods to create a new column with mapping from a dictionary in the Pyspark data frame: Using UDF () function Using map () function Method 1: Using UDF () function The most useful feature of Spark SQL & DataFrame that is used to extend the PySpark build-in capabilities is known as UDF, i. 5) session, for an insanely inefficient way of going about it: Mar 27, 2024 · pyspark. Aug 2, 2018 · data = data. Jun 4, 2020 · It complains that CSV data source does not support map data type. Returns Column A column of map type. As Example - i've this DF: rdd = sc. Such a list can be created using for comprehension on DataFrame columns: Jul 20, 2020 · Using pyspark, how to expand a column containing a variable map to new columns in a DataFrame while keeping other columns? Ask Question Asked 5 years, 4 months ago Modified 4 years, 8 months ago Mar 20, 2024 · Handling Missing Data When dealing with missing data within the “map” column, the “explode_outer” function comes in handy: from pyspark. It is used to apply operations over every element in a PySpark application like transformation, an update of the column, etc. map_from_entries(col) [source] # Map function: Transforms an array of key-value pair entries (structs with two fields) into a map. The first field of each entry is used as the key and the second field as the value in the resulting map column Parameters cols Column or str Column names or Column Returns Column A map of merged entries from other maps. Notes The input arrays for keys and values must have the same length and all elements in keys should not be null. By default, it throws an exception. parallelize([('123k', 1 Apr 18, 2023 · Introduction to PySpark Map PySpark MAP is a transformation in PySpark that is applied over each and every function of an RDD / Data Frame in a Spark Application. functions and Scala UserDefinedFunctions pyspark. map(f, preservesPartitioning=False) [source] # Return a new RDD by applying a function to each element of this RDD. create_map # pyspark. The create_map () function transforms DataFrame columns into powerful map structures for you to leverage. We will focus on one of the key transformations provided by PySpark, the map () transformation, which enables users to apply a function to each element in a dataset. I have uploaded data to a table. spark. sql. 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(). It can be done easily by using the create_map function with the map key column name and column name as arguments. The input columns are grouped into key-value pairs to form a map. There occurs various situations when you have numerous columns and you need to convert them to map-type columns. Parameters col Column or str Name of column or expression Returns Column Values of the map as an array. 5. This blog post describes how to create MapType columns, demonstrates built-in functions to manipulate MapType columns, and explain when to use maps in your analyses. I'd like to parse each row and return a new dataframe where each row is the parsed json Dec 26, 2019 · 6 Use create_map function to create a Map column and then explode it. These functions help you convert array or map columns into multiple rows, which is essential when working with nested data. In our case, we will use a UDF to map the values from the existing column to the new column using the dictionary. Column class provides several functions to work with DataFrame to manipulate the Column values, evaluate the boolean expression to filter rows, retrieve a value or part of a value from a DataFrame column, and to work with list, map & struct columns. May 15, 2020 · I need to be able to compare two dataframes using multiple columns. select("key") . 4. 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). functions ” Package. 1. All elements should not be null. If set to LAST_WIN, it uses the last map’s value. Sample dataset: data = [(1, 'N'), \\ (2, 'N'), \\ (3, 'C'), \\ (4, 'S'), \\ (5, 'North'), \\ (6, ' pyspark. In order to use Spark with Scala, you need to import org. , User Defined Function. valueContainsNullbool, optional indicates whether values can contain null (None) values. Nov 16, 2023 · Hey there! Maps are a pivotal tool for handling structured data in PySpark. Aug 23, 2024 · PySpark, the Python API for Apache Spark, provides powerful functions for data manipulation and transformation. Returns Column A new column of map type where each string in the original column is converted into a map. column. withColumn('letters_mapped', map_values_str(F. For this reason, spark can't easily infer what columns to create from the map. pySpark attempt # get PrimaryLookupAttributeValue values from reference table in a dictionary to compare them to df1. The return type is a new RDD or data frame where the Map function is applied. Uses the default column name col for elements in the array and key and value for elements in the map unless specified otherwise. Notes For duplicate keys in input maps, the handling is governed by spark. What is the most straightforward way to convert it to a struct (or, equivalently, define a new column with the same keys and values but as a struct type)? See the following spark-shell (2. RDD. com pyspark. from pyspark. May 14, 2018 · I want to know how to map values in a specific column in a dataframe. The mapping is achieved by retrieving the mapped value for every key in the original column. explode # pyspark. Nov 25, 2025 · In this article, I will explain how to explode an array or list and map columns to rows using different PySpark DataFrame functions explode(), I have just started using databricks/pyspark. So from "sale" dataframe like this: Converting a PySpark Map / Dictionary to Multiple Columns Python dictionaries are stored in PySpark map columns (the pyspark. You'll learn how to create, access, transform, and convert MapType columns using various PySpark operations. I wish to apply a mapping function to each e from pyspark. col2 Column or str Name of column containing a set of values. It takes as an input a map of existing column names and the corresponding desired column names. Jul 10, 2025 · PySpark MapType (map) is a key-value pair that is used to create a DataFrame with map columns similar to Python Dictionary (Dict) data structure. Default is colon (:). This function allows us to create a new column based on an existing column by applying a user-defined function (UDF) or a built-in function. Jun 11, 2022 · This mapping column is essentially a constant and, hence, we will have the same map in every row of the data frame. explode(col) [source] # Returns a new row for each element in the given array or map. If these conditions are not met, an exception will be thrown. map # RDD. valueType DataType DataType of the values in the map. 1. keyValueDelim Column or str, optional Delimiter to use to split key/value. types import IntegerType, FloatType, MapType 25 Since pyspark 3. Examples Example Working with Spark MapType Columns Spark DataFrame columns support maps, which are great for key / value pairs with an arbitrary length. ffunction A binary function (k: Column, v: Column) -> Column that defines the predicate. Sep 13, 2024 · In PySpark, Struct, Map, and Array are all ways to handle complex data. Here below is an example were two temporary columns are created (again using split): Parameters keyType DataType DataType of the keys in the map. This table is a single column full of strings. 0, you can use the withColumnsRenamed() method to rename multiple columns at once. This function expects two separate columns as input. To create the map, you want to use create_map. Replace Column Value with Dictionary (map) You can also replace column values from the python dictionary (map). Aug 5, 2022 · Using Spark 1. I want to create a new column (say col2) with the Jul 23, 2025 · In this article, we are going to learn about PySpark map () transformation in Python. Example 1: Display the attributes and features of MapType In this example, we will extract the keys and values of the features that are used in the DataFrame. Aug 7, 2022 · How to convert the data to map in PySpark, for dynamic columns? Input dataframe: May 15, 2017 · @AliAzG is there a way to Remove those rows from a pyspark dataframe whose entries from a column [of the pyspark] are not present in a dictionary's list of keys? Nov 5, 2025 · In Spark/PySpark from_json () SQL function is used to convert JSON string from DataFrame column into struct column, Map type, and multiple columns. 6, I have a Spark DataFrame column (named let's say col1) with values A, B, C, DS, DNS, E, F, G and H. Dec 22, 2016 · I need to creeate an new Spark DF MapType Column based on the existing columns where column name is the key and the value is the value. This function should return a boolean column that will be used to filter the input map. Mar 27, 2024 · PySpark Example: How to Get Size of ArrayType, MapType Columns in PySpark 1. Column ¶ Creates a new map column. pairDelim Column or str, optional Delimiter to use to split pairs. Why do we need these functions? All four functions share the same core purpose: they take each element inside pyspark. e. Default is comma (,). For information about array operations, see Jul 23, 2025 · The lit is used to add a new column to the DataFrame by assigning a literal or constant value, while create_map is used to convert selected DataFrame columns to MapType. You'll want to break up a map to multiple columns for performance gains and when writing data to different types of data stores. Solution: Get Size/Length of Array & Map DataFrame Column Spark/PySpark provides size() SQL function to get the size of the array & map type columns in DataFrame (number of elements in ArrayType or MapType columns). Apr 27, 2025 · This document covers the complex data types in PySpark: Arrays, Maps, and Structs. While conceptually simple, properly leveraging these functions can optimize your PySpark code and enable more efficient Jun 20, 2019 · Iterate over an array column in PySpark with map Asked 6 years, 5 months ago Modified 6 years, 5 months ago Viewed 31k times How I can access value from map using key, then how it is possible to map values from column in other dataframe using map dataframe. These allow you to easily extract just the keys or just the values from a Spark map into a list. In this blog, we’ll explore several essential PySpark functions: transform(), filter(), zip_with(), map_concat(), map_entries(), map_from_arrays(), map_from_entries(), map_keys(), and map_values(). Examples Example 1: Using default delimiters May 8, 2025 · 3. Apr 27, 2025 · Map and Dictionary Operations Relevant source files Purpose and Scope This document covers working with map/dictionary data structures in PySpark, focusing on the MapType data type which allows storing key-value pairs within DataFrame columns. By understanding their differences, you can better decide how to structure your data: Struct is best for fixed, known fields. I have a dataframe which looks like: See full list on sparkbyexamples. These data types allow you to work with nested and hierarchical data structures in your DataFrame operations. There may occur some situations in which we get data in the form of May 30, 2024 · To solve this problem, we can make use of the PySpark DataFrame API’s withColumn function. MapType class). 0. select(explode("payload")) . col('letters'), mapping)) But UDFs like this tend to be somewhat slow on large data sets in my experience. collect()) ] Create a new map with all of the fields Now use create_map as above, but use the information from keys to create the key-value pairs dynamically. Parameters col Column or str Name of column or expression Returns Column Keys of the map as an array. This guide explains how to apply transformations to RDDs using map, with examples and best practices for big data processing.

Pyspark map column. This table is a single column full of strings.