Spark Selectexpr Rename Column



Kafka Batch Queries [Spark 2. Sometimes there is need to process hierarchical data or perform hierarchical calculations. This release includes an enhanced UI built on Bootstrap 4, Localization, Per-Seat Pricing, Stripe v3, and a variety of other improvements. The Spark Streaming integration for Kafka 0. This naming convention is quite misleading for users since Spark MLlib API uses the "prediction" term only for actual predictions appended to the dataset during the transform phase. I need to concatenate two columns in a dataframe. This can be done easily using the function rename() [dplyr package]. 2] Only updated rows in result table to be written to sink 40. the answers suggesting to use cast, FYI, the cast method in spark 1. x as part of org. Sparkour is an open-source collection of programming recipes for Apache Spark. I would like to add this column to the above data. JSON is a very common way to store data. They are extracted from open source Python projects. 3 and the Scala API. the answers suggesting to use cast, FYI, the cast method in spark 1. 2 has many performance improvements in addition to critical bug fixes. Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Data Science in HD. Sep 30, 2016. If on is a string or a list of string indicating the name of the join column(s), the column(s) must exist on both sides, and this performs an inner equi-join. We need to provide the structure (list of fields) of the JSON data so that the Dataframe can reflect this structure:. To rename a dataframe using Spark, you just have to make use of the withColumnRenamed() method. We have set the session to gzip compression of parquet. Now, just let Spark derive the schema of the json string column. As organizations create more diverse and more user-focused data products and services, there is a growing need for machine learning, which can be used to develop personalizations, recommendations, and predictive insights. selectExpr Nov 25, 2015 This comment has been minimized. table and msckrepair table schema. xlsb raw data and once it is imported in SAS, the data structure will be as shown below. When those change outside of Spark SQL, users should call this function to invalidate the cache. The value to search can be a string literal, a function returning a string, or a reference to a column of String type. 2 has many improvements related to Streaming and unification of interfaces. format("com. What is difference between class and interface in C#; Mongoose. Manipulating data frames using the dplyr syntax is covered in detail in the Data Manipulation in R with dplyr and Joining Data in R with dplyr courses, but you'll spend the next chapter and a half covering all the important points. Spark has multiple ways to transform your data like rdd, Column Expression, udf and pandas udf. • It scans only the required columns and stores them in compressed in-memory columnar format. will create the value for that given row in the DataFrame. How to rename DataFrame columns name in pandas? 0 23 2018-01-25 Emp001 John Doe Chemist 1 24 2018-01-26 Emp00 William Spark Statistician C:. spark-shell --queue= *; To adjust logging level use sc. expressions. The following Scala code processes the file:. Apache Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. How can I filter them out within Spark(I found only one approach to turn Spark to table, use rename nodes and then turn table to spark, but that is not an real option)? Kind regard. returns a column of flight durations in hours instead of minutes. on – a string for join column name, a list of column names, , a join expression (Column) or a list of Columns. csv") val multipliedDF = df. Voila! We’re now able to remove all leading and trailing spaces in Excel (and Google Docs) no matter what type of space it is. A SparkDataFrame to be selected from. As our data is not provided as a plain text string, this needs to be the first step in our transformation. We can let Spark infer the schema of our csv data but proving pre-defined schema makes the reading process faster. The main use of the alter table command is to alter the table structure. Spark tracks all of this type information for you and offers a variety of ways, with which you can transform columns. How to count of category values in a column? Suppose, my data consists of a column token as ACH to know the count of these values in that column. // IMPORT DEPENDENCIES import org. selectExpr (“DEST_COUNTRY_NAME as newColumnName”, “DEST_COUNTRY_NAME”). The easiest is to use Spark’s from_json() function from the org. I tried it in the Spark 1. Computes statistics for numeric columns, including count, mean, stddev, min, and max. Experimental org. The Transform feature allows you to specify which columns to replicate, rename the columns at the destination, and even perform operations on the source data before replicating. I am attempting to create a binary column which will be defined by the value of the tot_amt column. Create a StringIndexer for each column and then run OneHotEncoder on them. Analista Sto Tomas. subset - optional list of column names to consider. Groups the DataFrame using the specified columns, so we can run aggregation on them. Spark doesn't support adding new columns or dropping existing columns in nested structures. In Apache Spark, we can read the csv file and create a Dataframe with the help of SQLContext. Introduced in Apache Spark 2. take() twice, converting to Pandas and slicing, etc. The caching functionality can be tuned using the setConf method in the SQLContext or HiveContext class. We will learn. As organizations create more diverse and more user-focused data products and services, there is a growing need for machine learning, which can be used to develop personalizations, recommendations, and predictive insights. But JSON can get messy and parsing it can get tricky. this answer edited Sep 17 '15 at 16:10 answered Sep 17 '15 at 15:54 Martin Senne 2,417 3 12 32 1 Can you advice me on how to proceed, if I need to cast and rename a whole bunch of columns (I have 50 columns, and fairly new to scala, not sure what is the best way to approach it without creating a massive duplication)? Some columns should stay. the answers suggesting to use cast, FYI, the cast method in spark 1. I noticed in Spark 2 (unlike 1. StructType objects define the schema of Spark DataFrames. Manipulating data frames using the dplyr syntax is covered in detail in the Data Manipulation in R with dplyr and Joining Data in R with dplyr courses, but you'll spend the next chapter and a half covering all the important points. If schema inference is needed, samplingRatio is used to determined the ratio of rows used for schema inference. As our data is not provided as a plain text string, this needs to be the first step in our transformation. csv") val multipliedDF = df. iat to access a DataFrame; Working with Time Series. Selecting columns The easiest way to manipulate data frames stored in Spark is to use dplyr syntax. Return a new SparkDataFrame range partitioned by the given column(s), using spark. Apache Spark is known as a fast, easy-to-use and general engine for big data processing that has built-in modules for streaming, SQL, Machine Learning (ML) and graph processing. The two configuration parameters for caching are. The revoscalepy module provides functions for data sources and data manipulation. Spark doesn't support adding new columns or dropping existing columns in nested structures. Typically, the first step I take when renaming columns with r is Often data you're working with has abstract column names, such as (x1, x2, x3…). how to rename the specific column of our choice by column index. json column is no longer a StringType, but the correctly decoded json structure, i. Thumbnail rendering works for any images successfully read in through the readImages function. NET application. map(lambda x: x[0]). These examples are extracted from open source projects. , but is there an easy transformation to do this?. Column = id Beside using the implicits conversions, you can create columns using col and column functions. Basic Example for Spark Structured Streaming and Kafka Integration With the newest Kafka consumer API, there are notable differences in usage. The following code examples show how to use org. Since then, a lot of new functionality has been added in Spark 1. 1 [CARBONDATA-780] - Alter table support for compaction through sort step [CARBONDATA-805] - Fix groupid,package name,Class name issues [CARBONDATA-856] - [Documentation] Alter Table - TABLE RENAME [CARBONDATA-857] - [Documentation] Alter Table - ADD COLUMNS. You can refer to the official Spark SQL programming guide for those formats. This operation is similar to the SQL MERGE command but has additional support for deletes and extra conditions in updates, inserts, and deletes. Analista Sto Tomas. sql Class DataFrame. This is an. This naming convention is quite misleading for users since Spark MLlib API uses the "prediction" term only for actual predictions appended to the dataset during the transform phase. All of this is submitted as one message per data set and ends up in the payload column in Spark, in some binary format. from pyspark. Left outer join. 3 サンプルデータ カラムfoo, bar, bazの3つを持つtmp_exampleテーブルを用意。. With an emphasis on improvements and new features in Spark 2. This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame. Cannot rename or drop columns that have dependent secondary indexes or Datastax Enterprise Search indexes. Connecting Event Hubs and Spark. Inserting a Column: Unlike other graphs, sparklines appear in a single cell. Sign in to view. The following statement inserts a new row into the link table and returns the last insert id:. Below are the uses of alter table command: You can use Netezza alter table command change or drop column defaults if any. Add Column Use the Spark SQL expression language to combine existing columns to create new columns or to create a new column of constant values. , Spark: The Definitive Guide, O'Reilly Media, 2018] (2/4) I select and selectExpr. col("DEST_COUNTRY_NAME")). Further, because these number based qualifiers are generally smaller (1 to 4 bytes) than column names, the disk size of tables is smaller which improves performance across the board. Comparing Spark Dataframe Columns. mismatched input ',' expecting in SelectExpr while reading columns from configuration file. setLogLevel(newLevel). spark-shell --queue= *; To adjust logging level use sc. Spark Streaming uses Spark Core's fast scheduling capability to perform streaming analytics MLlib Machine Learning Library Spark MLlib is a distributed machine learning framework on top of Spark Core that, due in large part to the distributed memory-based Spark architecture. This operation is similar to the SQL MERGE command but has additional support for deletes and extra conditions in updates, inserts, and deletes. Other columns are excluded from this working data set. Advanced Spark Structured Streaming - Aggregations, Joins, Checkpointing Dorian Beganovic November 27, 2017 Spark In this post we are going to build a system that ingests real time data from Twitter, packages it as JSON objects and sends it through a Kafka Producer to a Kafka Cluster. Alter Table or View — Databricks Documentation View Azure Databricks documentation Azure docs. With the addition of new date functions, we aim to improve Spark’s performance, usability, and operational stability. columns Renaming Columns Although we can rename a column in the above manner, it's often much easier (and readable) to use the withColumnRenamed method. 0 version, CarbonData integrated with Spark so that future versions of CarbonData can add enhancements based on Spark's new and improved capabilities. 6) it's possible to use filter/where on a DataFrame that previously had a column, but no longer has it in its schema due to a select() operation. In fillna , columns specified in cols that do not have matching data type are ignored. Thanks to the Kafka connector that we added as a dependency, Spark Structured Streaming can read a stream from Kafka:. Learn how to work with complex and nested data using a notebook in Databricks. Active 1 year, 11 months ago. SparkSession import org. SQL-Standard Based Authorization is one of the available Authorization methods for Spark SQL with spark-authorizer. 2、 columns 返回一个string类型的数组,返回值是所有列的名字 3、 dtypes返回一个string类型的二维数组,返回值是所有列的名字以及类型 4、 explan()打印执行计划 物理的. Simply put, Spark is a fast and general engine for large-scale data processing. sql(" DROP TABLE IF EXISTS " + final_table + " PURGE ") # columns to avoid adding to the table as they take a lot of resources # this is the list of parsed columns after exploded, so arrays (as child_fields specified) can be excluded if they have been exploded previously. Alter Table Alter Column. dilipbiswal changed the title [SPARK-11619] cannot use UDTF in DataFrame. As organizations create more diverse and more user-focused data products and services, there is a growing need for machine learning, which can be used to develop personalizations, recommendations, and predictive insights. SQLContext(). A user can define an arbitrary number of aggregate functions which can be computed at the same time. * We will be able to access the elements in Data Frame by passing column name as string type or col type. This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame. Here is the link. stack (self, level=-1, dropna=True) [source] ¶ Stack the prescribed level(s) from columns to index. If tot_amt <(-50) I would like it to return 0 and if tot_amt > (-50) I would like it to return 1 in a new column. It is an aggregation where one of the grouping columns values transposed into individual columns with distinct data. functions import col new_df = old_df. json column is no longer a StringType, but the correctly decoded json structure, i. warn_missing print a message if any of the old names are not actually present in x. First, we’ll use the rename function to rename our columns, and the select function to select the columns we need. In Spark, dataframe is actually a wrapper around RDDs, the basic data structure in Spark. I Change the orderof rows based on the values in columns [M. The Column class represents a tree of operations to be applied to each input record: things like mathematical operations, comparisons, etc. 5, with more than 100 built-in functions introduced in Spark 1. # Implementation of ANOVA function: calculates the degrees of freedom, F-value, eta squared and omega squared values. Selects a set of columns. Often data you’re working with has abstract column names, such as (x1, x2, x3…). Also known as a contingency table. JSON is a very common way to store data. SparkSession import org. The main use of the alter table command is to alter the table structure. 8 collections library a case of "the longest suicide note in history"?. How to pass column names in selectExpr through one or more string parameters in spark using scala? spark streaming spark-sql scala spark spark dataframe merge Question by swapan1189 · 14 hours ago ·. We have set the session to gzip compression of parquet. This is a PostgreSQL extension to SQL. Here you apply a function to the "billingid" column. 4 library between 1. Enter the desired column width in the "Standard column width" field. withcolumnrenamed spark one multiple example columns column scala apache-spark dataframe apache-spark-sql How to sort a dataframe by multiple column(s)? Is the Scala 2. To customize your replication, click the Transform button in the Tables section and customize the replication. Spark posexplode_outer(e: Column) creates a row for each element in the array and creates two columns "pos' to hold the position of the array element and the 'col' to hold the actual array value. on - a string for join column name, a list of column names, , a join expression (Column) or a list of Columns. if I want the 20th to 30th rows of a dataframe in a new DF? I can think of a few ways - adding an index column and filtering, doing a. Spark & R: data frame operations with SparkR Published Sep 21, 2015 Last updated Apr 12, 2017 In this third tutorial (see the previous one) we will introduce more advanced concepts about SparkSQL with R that you can find in the SparkR documentation , applied to the 2013 American Community Survey housing data. Hi I have a dataframe (loaded CSV) where the inferredSchema filled the column names from the file. In Apache Spark, we can read the csv file and create a Dataframe with the help of SQLContext. cannot construct expressions). In this blog post, we highlight three major additions to DataFrame API in Apache Spark 1. I tried it in the Spark 1. Renaming an index using the system stored procedure sp_rename. Rule is if column contains “yes” then assign 1 else 0. selectExpr() takes SQL expressions as a string: with the SQL as keyword being equivalent to the. functions, they enable developers to easily work with complex data or nested data types. The number of distinct values for each column should be less than 1e4. As organizations create more diverse and more user-focused data products and services, there is a growing need for machine learning, which can be used to develop personalizations, recommendations, and predictive insights. This section provides a reference for Apache Spark SQL and Delta Lake, a set of example use cases, and information about compatibility with Apache Hive. subset - optional list of column names to consider. In Apache Spark, we can read the csv file and create a Dataframe with the help of SQLContext. Arguments x. Example to Rename or Change Column Labels. To rename a dataframe using Spark, you just have to make use of the withColumnRenamed() method. _ import org. grouped_df = joined_df. Typically, the first step I take when renaming columns with r is Often data you're working with has abstract column names, such as (x1, x2, x3…). "Data scientists spend more time wrangling data than making models. Each new release of Spark contains enhancements that make use of DataFrames API with JSON data more convenient. Rename Rename the selected column. datestamp) \. , but is there an easy transformation to do this?. selectExpr (“DEST_COUNTRY_NAME as newColumnName”, “DEST_COUNTRY_NAME”). The following are code examples for showing how to use pyspark. In this tutorial we will learn how to rename the column of dataframe in pandas. Andrew Ray. selectExpr(getColumn(beginDate, endDate, x. Once created, it can be manipulated using the various domain-specific-language (DSL) functions defined in: DataFrame (this class), Column, and functions. The word "graph" can also describe a ubiquitous data structure consisting of. Often data you’re working with has abstract column names, such as (x1, x2, x3…). This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame. There will be a new column added to the dataframe with Boolean values ,we can apply filter to get only those are true. Then the df. # Implementation of ANOVA function: calculates the degrees of freedom, F-value, eta squared and omega squared values. as simply changes the view of the data that is passed into typed operations (e. In this article, we will discuss on the commonly used Hadoop Hive commands. The following code examples show how to use org. Pivoting is used to rotate the data from one column into multiple columns. As there are multiple columns in my report and some of which contains long texts without any space or separator. column: S4 class that represents a SparkDataFrame column: Column-class: S4 class that represents a SparkDataFrame column: column-method: S4 class that represents a SparkDataFrame column: columnfunctions: A set of operations working with SparkDataFrame columns: columns: Column Names of SparkDataFrame: columns-method: Column Names of. The Spark variant of SQL's SELECT is the. selectExpr(“DEST_COUNTRY_NAME as newColumnName”, “DEST_COUNTRY_NAME”). Return a new SparkDataFrame range partitioned by the given column(s), using spark. alias(new_name) if s == column_to_change else s for s in old_df. I have tried different techniques like normal Logistic Regression, Logistic Regression with Weight column, Logistic Regression with K fold cross validation, Decision trees, Random forest and Gradient Boosting to see which model is the best. This article describes Spark Streaming example on Consuming messages from Kafa and Producing messages to Kafka in JSON format using from_json and to_json Spark functions respectively. In particular, the withColumn and drop methods of the Dataset class don't allow you to specify a column name different from any top level columns. Therefore the Spark statistics node doesn't recognize any usable columns. Previous Window Functions In this post we will discuss about writing a dataframe to disk using the different formats like text, json , parquet ,avro, csv. how to rename the specific column of our choice by column index. Below are the uses of alter table command: You can use Netezza alter table command change or drop column defaults if any. we will use | for or, & for and , ! for not. Sep 30, 2016. Subsequent dataframe join dont work. SQL: ALTER TABLE Statement. setLogLevel(newLevel). mapper, index, columns: dict-like or function, optional dict-like or functions transformations to apply to that axis’ values. Join a community of over 2. Renaming Columns. This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame. If you want to rename a single column and keep the rest as it is: from pyspark. In the example below, we are simply renaming the Donut Name column. withColumn ("Destination", df. 0 ships with the second-generation Tungsten engine, which aims to improve Spark execution by optimizing Spark jobs for CPU and memory efficiency, through a technique called “whole-stage code generation”. Substitute Obscure sensitive information from view by substituting a random string of characters for the actual data in the selected column. Union, Minus, and Intersect: The attribute/column names from the left operand are used. Recover from query failures. This article describes Spark Streaming example on Consuming messages from Kafa and Producing messages to Kafka in JSON format using from_json and to_json Spark functions respectively. I get the expected result when i write it using selectExpr() but when i add the same logic in. Without them, if there were a column named alphabet, it would also match, and the replacement would be onebet. Here are a few examples of parsing nested data structures in JSON using Spark DataFrames (examples here done with Spark 1. DataFrame A distributed collection of data grouped into named columns. Rename – rename an existing column or field in a nested struct Update – widen the type of a column, struct field, map key, map value, or list element Reorder – change the order of columns or fields in a nested struct Iceberg schema updates are metadata changes. Apache Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. The main use of the alter table command is to alter the table structure. This can be done easily using the function rename() [dplyr package]. select(*[col(s). SEMI JOIN Select only rows from the side of the SEMI JOIN where there is a match. The caching functionality can be tuned using the setConf method in the SQLContext or HiveContext class. js: Find user by username LIKE value. cannot construct expressions). A dataframe in Spark is similar to a SQL table, an R dataframe, or a pandas dataframe. • Spark SQL automatically selects a compression codec for each column based on data statistics. 这里的rename是pandas. Interestingly, we can also rename a column this way. This naming convention is quite misleading for users since Spark MLlib API uses the "prediction" term only for actual predictions appended to the dataset during the transform phase. 0 using PySpark. Often data you’re working with has abstract column names, such as (x1, x2, x3…). I can rename this 'assignee. Spark Dataframe API: pyspark. withColumn ("Destination", df. order_customer_id. Using Spark StructType – To rename a nested column in Dataframe Changing a column name on nested data is not straight forward and we can do this by creating a new schema with new DataFrame columns using StructType and use it using cast function as shown below. Spark Dataframe API: pyspark. But JSON can get messy and parsing it can get tricky. Spark doesn't support adding new columns or dropping existing columns in nested structures. In fact, one has likely plotted simple lines and curves using "graphing paper" or a "graphing calculator" before. columns Renaming Columns Although we can rename a column in the above manner, it’s often much easier (and readable) to use the withColumnRenamed method. I can perform almost all the SQL operations on it in SPARK-SQL. This is something that you will need to for sure in Scala, since the machine learning models will need two columns named features and label in order to be trained. When you're selecting a column using the df. The value to search can be a string literal, a function returning a string, or a reference to a column of String type. def crosstab (self, col1, col2): """ Computes a pair-wise frequency table of the given columns. In the previous tutorial, you learned how to add one or more rows into a table using the INSERT statement with a list of column values specified in the VALUES clause. In this article, we will discuss on the commonly used Hadoop Hive commands. for example, a dataframe with a string column having value "8182175552014127960" when casted to bigint has value "8182175552014128100". withColumn() i get TypeError: Column is not iterable. How to select particular column in Spark(pyspark)? Ask Question Asked 3 years, 9 months ago. In other words, I … More. will create the value for that given row in the DataFrame. My data frame is holding about 400 columns, with 1000-1M rows. we will use | for or, & for and , ! for not. These columns basically help to validate and analyze the data. Typically, the first step I take when renaming columns with r is Often data you're working with has abstract column names, such as (x1, x2, x3…). sql import HiveContext, Row #Import Spark Hive SQL hiveCtx = HiveContext(sc) #Cosntruct SQL context. Since then, a lot of new functionality has been added in Spark 1. The number of distinct values for each column should be less than 1e4. How to select particular column in Spark(pyspark)? Ask Question Asked 3 years, 9 months ago. In this tutorial, I show and share ways in which you can explore and employ five Spark SQL utility functions and APIs. StructType objects contain a list of StructField objects that define the name, type, and nullable flag for each column in a DataFrame. Sort ascending Sort rows by the selected column in ascending order. Here we show how to load csv files. my selectexpr statement: val df = df2. How does one slice a Spark DF horizontally by index (and not by column properties)? For eg. Download with Google Download with. Combine useful columns into a column named “features” on which models will be run. This is a PostgreSQL extension to SQL. The first row will be used if samplingRatio is None. You can also use the. functions: 1. In the couple of months since, Spark has already gone from version 1. Scaling columns can be done for Spark DataFrame, but the implementation can be much more involved compared with using scikit-learn functions for Pandas DataFrame. sql import HiveContext, Row #Import Spark Hive SQL hiveCtx = HiveContext(sc) #Cosntruct SQL context. csv") val multipliedDF = df. Machine Learning. 3 and the Scala API. I would need to get "Type", "DeviceID" and "OfficeAddress". For example, if value is a character, and subset contains a non-character column, then the non-character column is simply ignored. Description The SQLite ALTER TABLE statement is used to add, modify, or drop/delete columns in a table. In simple terms, it is same as a table in relational database or an Excel sheet with Column headers. Row A row of data in a DataFrame. TEMPORARY The created table will be available only in this session and will not be persisted to the underlying metastore, if any. column_name. Apache Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. The following are code examples for showing how to use pyspark. The word "graph" can also describe a ubiquitous data structure consisting of. A named pair of the form new_column_name = existing_column saveAsTable, schema, selectExpr, select. Hi I have a dataframe (loaded CSV) where the inferredSchema filled the column names from the file. Column A column expression in a DataFrame. You may say that we already have that, and it's called groupBy , but as far as I can tell, groupBy only lets you aggregate using some very limited options. In this article…. When those change outside of Spark SQL, users should call this function to invalidate the cache. Left outer join is a very common operation, especially if there are nulls or gaps in a data. Each new release of Spark contains enhancements that make use of DataFrames API with JSON data more convenient. These arguments can either be the column name as a string (one for each column) or a column object (using the df. I get the expected result when i write it using selectExpr() but when i add the same logic in. 07 】 リヤウィング専用トランクスポイラー [材質] FRP(素地),送料無料 YSS RACING その他のスポーツスター リアサスペンション関連パーツ Sports Line G-Series 366. Structured Streaming in Apache Spark: Easy, Fault Tolerant and Scalable Stream Processing 10th Extremely Large Databases Conference (XLDB) October 11th 2017, Clermont-Ferrand, France. For performance reasons, Spark SQL or the external data source library it uses might cache certain metadata about a table, such as the location of blocks. Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool. As an example, similar to the Spark data scaling example, the following code uses the Spark MinMaxScaler, VectorAssembler, and Pipeline objects to scale Spark DataFrame columns:. Let's try with an example: Create a dataframe:. Rename the column name_temp to name Create a new column called units_sold based on a rounding up of an integer division of SALES_AMOUNT and PRICE Register a UDF calculate_units_sold that divides the columns SALES_AMOUNT and PRICE and returns a rounded-up integer. Spark has multiple ways to transform your data like rdd, Column Expression, udf and pandas udf. A string containing a SQL expression.