How to Create udf() in PySpark
UDF (User-Defined Function) is used for custom data transformations or calculations which are not available in the built-in Spark SQL functions
Compare cache() and persist() in PySpark
cache() and persist() allow you to persist intermediate or frequently used data in memory, in order to improve the performance of subsequent operations.
How to Consolidate Data with PySpark’s Collect()
In PySpark collect() function is used to retrieve all the elements of Dataframe or Dataset and return them as a local collection or array in the driver program
How to Use union() Function in PySpark
the union() function is used to combine two dataframes with the same schema. It creates a new dataframe that includes all the rows from both dataframes
Compare foreach() and foreachPartition()
foreach() and foreachPartition() are used to apply function to each element of Dataframe or RDD. they differ in behavior and usage when used with distributed data
Compare map() vs mapPartitions() with Example
map() and mapPartitions() are used to apply a transformation on each element of a Dataframe or RDD. there are some differences in their behavior and usage.
How to Use Distinct() Function in PySpark
distinct() function is used to retrieve unique rows from a Dataframe. It returns a new Dataframe with distinct rows based on all the columns of original df
How to Use withColumnRenamed() Function in PySpark
withColumnRenamed() function is used to rename a column in df. It allows you to change the name of a column to new name while keeping the rest of the df…
How to Use withColumn() Function in PySpark
withColumn() function is used to add a new column or replace an existing column in a df. It allows you to transform and manipulate data by applying expressions
How to Use Array in PySpark
arrays in PySpark allows you to handle collection of values within a Dataframe column. PySpark provides various functions to manipulate and extract information