# Converting numbers or binary strings into their corresponding hexadecimal using PySpark.

PySpark provides, the hex function stands out when it comes to data transformations related to hexadecimal representation. This article sheds light on its utility, practical examples, and real-world use-cases. In PySpark, the hex function is used to convert numbers or binary strings into their corresponding hexadecimal representation.

Example of converting numbers to hexadecimal:

from pyspark.sql import SparkSession
from pyspark.sql.functions import hex
spark = SparkSession.builder \
.appName("Learning @ Freshers.in PySpark Hex Function") \
.getOrCreate()
data = [(10,), (255,), (1000,)]
df = spark.createDataFrame(data, ["numbers"])
df.withColumn("hex_value", hex(df["numbers"])).show()


Output

+-------+---------+
|numbers|hex_value|
+-------+---------+
|     10|        A|
|    255|       FF|
|   1000|      3E8|
+-------+---------+


#### Use Case: MAC address transformation

One practical scenario where hex might be useful is when dealing with MAC addresses. Assume you’ve been given a dataset of MAC addresses without the usual colon (“:”) delimiters, and you’re tasked with extracting and converting each byte.

Let’s simulate this:

data = [("AABBCCDDEEFF",), ("112233445566",)]
# Extract and convert each byte pair
for i in range(6):
df_mac.show()


Output

+------------+------+------+------+------+------+------+
+------------+------+------+------+------+------+------+
|AABBCCDDEEFF|  4141|  4242|  4343|  4444|  4545|  4646|
|112233445566|  3131|  3232|  3333|  3434|  3535|  3636|
+------------+------+------+------+------+------+------+


While this example is a simplification, in actual network datasets, the hex function can be essential in data transformation and cleaning tasks.

#### When and where to use hex?

Data Cleaning and Transformation: Especially in IT and network datasets, where hexadecimal representation is common.

Hashing and Encryption: When dealing with hashes or encrypted data, the hex function can aid in data transformation.

Binary Data: If your dataset contains raw binary data or BLOBs, converting it into a human-readable hex format can be useful for inspection or storage.

Spark important urls to refer

Author: user