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)
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|
+------------+------------------------+