I have pandas 1.3.2 installed.
But my util.py file not taking the latest version.

new_DF=old_DF .select(col(“id”), col(“COL1”), col(“COL2”), ).distinct() new_JSON_DF = new_DF .withColumn(“PROP”,struct(col(“COL1”),col(“COL2”))) .drop(“COL1”, “COL2″) Col1, Col2 could have nulls. If data is like following id COL1 COL2 1 null def 2 abc null 3 null null I want the output in new JSON DF to be like { id : 1 PROP : { “COL2” : “def” } […]

I’ve a dataset of the form :

I’m unsure how to handle some errors that I’m getting when converting a pandas dataframe into a PySpark dataframe. My Pandas dataframe has a column “array_output”, which is an array created from an image using OpenCV. It looks something like this:

I am working with a large dataset that includes multiple unique groups of data identified by a date and a group ID. Each group contains multiple IDs, each with several attributes. Here’s a simplified structure of my data:

I am working with a large dataset that includes multiple unique groups of data identified by a date and a group ID. Each group contains multiple IDs, each with several attributes. Here’s a simplified structure of my data:

I’m literally new to data engineering where I’m using Python, PySpark and Pandas to create a data frame and I’m since a very long time I’m blocked and could not put my head around it. It is a simple problem but I’m stuck here.

I tried to run this simple Spark session creation command in my Jupyter notebook –