![]() You can use this function to filter the DataFrame rows by single or multiple conditions, to derive a new column, use it on when ().otherwise () expression e.t.c. Still you can use raw SQL: import .hive.HiveContext val sqlContext new HiveContext (sc) // Make sure you use HiveContext import sqlContext.implicits. Spark SQL This page gives an overview of all public Spark SQL API. In Spark & PySpark like () function is similar to SQL LIKE operator that is used to match based on wildcard characters (percentage, underscore) to filter the rows. ![]() In this article, you have learned how to use Pyspark SQL “ case when” and “ when otherwise” on Dataframe by leveraging example like checking with NUll/None, applying with multiple conditions using AND (&), OR (|) logical operators. 1 Answer Sorted by: 10 provides like method but as for now (Spark 1.6.0 / 2.0.0) it works only with string literals. ![]() otherwise(df.gender).alias("new_gender")) ("Robert",None,400000), ("Maria","F",500000),įrom import when,colĭf2 = df.withColumn("new_gender", when(df.gender = "M","Male")ĭf2=df.select(col("*"),when(df.gender = "M","Male") This article is a quick guide for understanding the column functions like, ilike, rlike and not like Using a sample pyspark Dataframe ILIKE (from 3.3.0) SQL ILIKE expression (case. To explain this I will use a new set of data to make it simple.ĭf5.withColumn(“new_column”, when((col(“code”) = “a”) | (col(“code”) = “d”), “A”) We often need to check with multiple conditions, below is an example of using PySpark When Otherwise with multiple conditions by using and (&) or (|) operators. Multiple Conditions using & and | operator Its result include strings that are case-insensitive and follow the mentioned pattern. ![]() "ELSE gender END as new_gender from EMP").show()Ģ.3. The PostgreSQL ILIKE operator is used query data using pattern matching techniques. Usage would be like when (condition).otherwise (default). ANY or SOME or ALL: If ALL is specified then ilike returns true if str matches all patterns, otherwise returns true if it matches at least one pattern. Spark.sql("select name, CASE WHEN gender = 'M' THEN 'Male' " + PySpark when () is SQL function, in order to use this first you should import and this returns a Column type, otherwise () is a function of Column, when otherwise () not used and none of the conditions met it assigns None (Null) value. You can also use Case When with SQL statement after creating a temporary view. Spark SQL Using LIKE Operator similar to SQL Like ANSI SQL, in Spark also you can use LIKE Operator by creating a SQL view on DataFrame, below example filter table rows where name column contains rose string. "WHEN gender = 'F' THEN 'Female' WHEN gender IS NULL THEN ''" +ĭf4 = df.select(col("*"), expr("CASE WHEN gender = 'M' THEN 'Male' " + Also can you tell us or guide for a proper solution for the same. Share Improve this answer Follow answered at 9:08 Robert Kossendey 6,430 2 12 41 I am not able to understand what are you trying to state here. rlike () Syntax Following is a syntax of rlike () function, It takes a literal regex expression string as a parameter and returns a boolean column based on a regex match. It is similar to regexplike () function of SQL. When() function take 2 parameters, first param takes a condition and second takes a literal value or Column, if condition evaluates to true then it returns a value from second param.įrom import expr, colĭf3 = df.withColumn("new_gender", expr("CASE WHEN gender = 'M' THEN 'Male' " + Example: SELECT ilike ('Spark', 'Park') Returns true. It can be used on Spark SQL Query expression as well. Usage would be like when(condition).otherwise(default). PySpark when() is SQL function, in order to use this first you should import and this returns a Column type, otherwise() is a function of Column, when otherwise() not used and none of the conditions met it assigns None (Null) value. Using w hen() o therwise() on PySpark DataFrame. Applies to: Databricks SQL Databricks Runtimeĭefines a table using the definition and metadata of an existing table or view.ĭelta Lake does support CREATE TABLE LIKE in Databricks SQL and Databricks Runtime 13.0 and later.īefore Databricks Runtime 13.0 use CREATE TABLE AS.Spark = ('').getOrCreate()ĭata = [("James","M",60000),("Michael","M",70000),
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