Before going deeper, at first, we need to see how to create the Spark session. It can be done as follows:

spark = SparkSession
         .builder
         .appName("PCAExample")
         .getOrCreate()

Now under this code block, you should place your codes, for example:

data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),),
         (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
         (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
 df = spark.createDataFrame(data, ["features"])

 pca = PCA(k=3, inputCol="features", outputCol="pcaFeatures")
 model = pca.fit(df)

 result = model.transform(df).select("pcaFeatures")
 result.show(truncate=False)

The preceding code demonstrates how to compute principal components on a RowMatrix and use them to project the vectors into a low-dimensional space. For a clearer picture, refer to the following code that shows how to use the PCA algorithm on PySpark:

import os
import sys

try:
from pyspark.sql import SparkSession
from pyspark.ml.feature import PCA
from pyspark.ml.linalg import Vectors
print ("Successfully imported Spark Modules")

except ImportErrorase:
print ("Can not import Spark Modules", e)
 sys.exit(1)

spark = SparkSession
   .builder
   .appName("PCAExample")
   .getOrCreate()

data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),),
    (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
    (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
df = spark.createDataFrame(data, ["features"])

pca = PCA(k=3, inputCol="features", outputCol="pcaFeatures")
model = pca.fit(df)

result = model.transform(df).select("pcaFeatures")
result.show(truncate=False)

spark.stop()

The output is as follows: