Fix lib_test_utils + Added OS Env for PYSPARK & added libraries in README

README.md

@@ -3,6 +3,8 @@
 ## Installation
    - [Python 3.10](https://www.python.org/downloads/)
      - pyspark=3.1.1
+     - findspark
+     - pyspark-test
    - [Spark 3.1.1](https://spark.apache.org/downloads.html)
    - [Hadoop 3.3.6](https://hadoop.apache.org/releases.html)
    - [Java JDK 11](https://www.oracle.com/java/technologies/downloads/#java11)

src/test_pyspark_training/conftest.py

@@ -1,4 +1,5 @@
 import os
+import sys
 import findspark
 import logging
 import pytest
@@ -7,9 +8,17 @@ from pyspark.sql import SparkSession
 
 @pytest.fixture
 def spark_session(request):
+    """
+    Return a Spark Session
+    :param request:
+    :return: Spark session
+    """
+    os.environ['PYSPARK_PYTHON'] = sys.executable
+    os.environ['PYSPARK_DRIVER_PYTHON'] = sys.executable
     os.environ["JAVA_HOME"] = "C:\\Program Files\\Java\\jdk-11"
     os.environ["SPARK_HOME"] = "C:\\SPARK\\spark-3.1.1-bin-hadoop3.2"
     os.environ["HADOOP_HOME"] = "C:\\SPARK\\hadoop"
+
     findspark.init()
 
     spark = SparkSession.builder.master("local[*]").getOrCreate()

src/test_pyspark_training/lib_test_utils.py

@@ -1,15 +1,16 @@
+from pyspark.sql import DataFrame
 from pyspark_test import assert_pyspark_df_equal
 
 
-def assert_df_equal(df1, df2):
+def assert_df_equal(actual_df: DataFrame, expected_df: DataFrame) -> None:
 
     try:
-        assert df1.schema == df2.schema
+        assert actual_df.schema == expected_df.schema
     except AssertionError:
         print('Error Schema')
-        print('df1\n')
-        df1.printSchema()
-        print('df2\n')
-        df2.printSchema()
+        print('Actual :\n')
+        actual_df.printSchema()
+        print('Expected :\n')
+        expected_df.printSchema()
 
-    assert_pyspark_df_equal(df1, df2)
+    assert_pyspark_df_equal(actual_df, expected_df)

src/test_pyspark_training/test_output_dataset_1/test_remove_extra_spaces/test_remove_extra_spaces.py

@@ -19,7 +19,6 @@ def test_remove_extra_spaces(spark_session):
     ]
     input_df = spark_session.createDataFrame(input_data, input_schema)
 
-
     expected_schema = T.StructType(
         [
             T.StructField('name', T.StringType(), False),