I have a dataframe like below. I am trying to extract the next word after function or var My code is here. as it is capture only one word, the final row returns only AWS and not Twitter. So I would like to capture all matching. My spark version is less than 3, so I tried df.withColumn(‘output’, f.expr(“regexp_extract_all(js, ‘(func)s+(w+)|(var)s+(w+)’, 4)”)).show()
Tag: apache-spark
PySpark create a json string by combining columns
I have a dataframe. I would like to perform a transformation that combines a set of columns and stuff into a json string. The columns to be combined is known ahead of time. The output should look like something below. Is there any sugggested method to achieve this? Appreciate any help on this. Answer You can create a struct type
Spark ERROR in cluster: ModuleNotFoundError: No module named ‘cst_utils’
I have a Spark program with python. The structure of the program is like this: Each cst_utils.py,bn_utils.py,ep_utils.py has a function called Spark_Func(sc). In main I make a Spark Context, sc, and send it to the each Spark_Func like this: I config Spark cluster with two Slaves and One Master, all of them have Ubuntu 20.04 OS. I set Master IP
Column with column names for nulls in row
I want to add new column “Null_Values” in PySpark dataframe as below Answer or
Logical with count in Pyspark
I’m new to Pyspark and I have a problem to solve. I have a dataframe with 4 columns, being customers, person, is_online_store and count: customer PersonId is_online_store count afabd2d2 4 true 1 afabd2d2 8 true 2 afabd2d2 3 true 1 afabd2d2 2 false 1 afabd2d2 4 false 1 I need to create according to the following rules: If PersonId count(column)
Calculate difference between date column entries and date minimum Pyspark
I feel like this is a stupid question, but I cannot seem to figure it out, so here goes. I have a PySpark data frame and one of the columns consists of dates. I want to compute the difference between each date in this column and the minimum date in the column, for the purpose of filtering to the past
Import pipe delimited txt file into spark dataframe in databricks
I have a data file saved as .txt format which has a header row at the top, and is pipe delimited. I am working in databricks, and am needing to create a spark dataframe of this data, with all columns read in as StringType(), the headers defined by the first row, and the columns separated based on the pipe delimiter.
How to get conditional values into new column from several external lists or arrays
I have the following dataframe: To which I have to create an additional column new_col_cond that is dependent on the values of multiple external lists/arrays (I have also tried with dictionaries), for example: The new column depends on the value of ratio and selects from either array according to id as index. I have tried: with errors coming. I assume
Summarizing labels at time steps based on current and past info
Given the following input dataframe A dataframe which looks like this needs to be constructed The input dataframe has 10s of millions of records. Some details which are seen in the example above (by design) npos is the size of the vector to be constructed in the output pos is guaranteed to be in [0,npos) at each time step (elap)
Create column from array of struct Pyspark
I’m pretty new to data processing. I have a deeply nested dataset that have this approximately this schema : For the array, I will receive something like this. Keep in mind that the length is variable, I might receive no value or 10 or even more Is there a way to transform the schema to : with VAT and fiscal1