I have a DataFrame with a binary column that represents the hexadecimal encoding of an initial string:    random_id random_id_cesu8     123456789012 [31 32 33 34 35 36 37 38 39 30 31 32]    The random_id_cesu8 column contains the binary representation of the random_id string encoded in UTF-8 and displayed as a list of byte values in hexadecimal format. I want to transform the random_id_cesu8 column into a single concatenated hexadecimal string: 313233343536373839303132, which is the concatenation of each individual byte value from the random_id_cesu8 list. I’ve tried multiple approaches, but they all result in the original random_id value (123456789012) instead of the desired concatenated hexadecimal string (313233343536373839303132). How can I correctly achieve this transformation?
Here's a solution using PySpark to convert the binary column to its hexadecimal representation: from pyspark.sql import functions as F # Create DataFrame with the initial value df = spark.createDataFrame([("123456789012",)], ["value"]) # Convert the value to UTF-8 encoding and then to hex df = df.withColumn( "encoded_hex", F.hex(F.encode(F.col("value"), 'utf-8')), ) # Display the resulting DataFrame df.show(truncate=False)  Explanation:  encode(df.value, 'utf-8'): This function encodes the string value into a binary format using UTF-8 encoding. hex(): This function converts the binary data into its hexadecimal representation.  The result will be a DataFrame with the encoded_hex column containing the desired hexadecimal string 313233343536373839303132. +------------+------------------------+ |value |encoded_hex | +------------+------------------------+ |123456789012|313233343536373839303132| +------------+------------------------+ 