Add a PySpark example in the quickstart

docs/Quickstart.md

@@ -106,6 +106,54 @@ Sampling has also an operator called `TABLESAMPLE`, which can be expressed in bo
 spark.sql("SELECT avg(price) FROM ecommerce_qbeast TABLESAMPLE(10 PERCENT)").show()
 ```
 
+## Python
+
+Similar to Scala and SQL, you can also use PySpark to operate on Qbeast tables. 
+Here, you can find an end-to-end example following the same operations demonstrated earlier on this page.
+
+```python
+from pyspark.sql import SparkSession
+
+# Initialize Spark session with Qbeast and Delta support, running locally
+spark = SparkSession.builder \
+    .appName("QbeastExample") \
+    .master("local[*]") \
+    .config("spark.jars.packages", "io.qbeast:qbeast-spark_2.12:0.6.0,io.delta:delta-spark_2.12:3.1.0") \
+    .getOrCreate()
+
+qbeast_table_path = "/tmp/qbeast-test-data/qtable"
+
+# Creating a temporary view for the ecommerce data of October
+ecommerce_october = spark.read \
+    .format("csv")\
+    .option("header", "true") \
+    .option("inferSchema", "true") \
+    .load("src/test/resources/ecommerce100K_2019_Oct.csv")
+
+# Creating a Qbeast table using PySpark
+ecommerce_october.write \
+    .format("qbeast") \
+    .mode("overwrite") \
+    .option("columnsToIndex", "user_id,product_id") \
+    .option("cubeSize", "500") \
+    .save(qbeast_table_path)
+
+# Reading the ecommerce data for November
+ecommerce_november = spark.read \
+    .format("csv") \
+    .option("header", "true") \
+    .option("inferSchema", "true") \
+    .load("src/test/resources/ecommerce300k_2019_Nov.csv")
+
+# Inserting data into the Qbeast table using PySpark
+ecommerce_november.write \
+    .format("qbeast") \
+    .mode("append") \
+    .save(qbeast_table_path)
+
+sampled_data = spark.read.format("qbeast").load(qbeast_table_path).sample(0.10)
+sampled_data.selectExpr("avg(price)").show()
+```
 
 ## Analyze and Optimize