style: format code with Autopep8, Black and Ruff Formatter

This commit fixes the style issues introduced in b882bb0 according to the output
from Autopep8, Black and Ruff Formatter.

Details: https://github.com/ajschofield/de-project-bentley/pull/84

1 files changed, 59 insertions, 35 deletions

diff --git a/tests/test_transform_lambda.py b/tests/test_transform_lambda.py
index 7de1bf3..5121905 100644
--- a/tests/test_transform_lambda.py
+++ b/tests/test_transform_lambda.py
@@ -6,45 +6,69 @@ import os
 import boto3
 import numpy as np
 
-@pytest.fixture(scope='class')
+
+@pytest.fixture(scope="class")
 def aws_credentials():
-    os.environ["AWS_ACCESS_KEY_ID"] = 'testing'
-    os.environ["AWS_SECRET_ACCESS_KEY"] = 'testing'
-    os.environ["AWS_SECURIT_TOKEN"] = 'testing'
-    os.environ["AWS_SESSION_TOKEN"] = 'testing'
-    os.environ["AWS_DEFAULT_REGION"]= 'eu-west-2'
+    os.environ["AWS_ACCESS_KEY_ID"] = "testing"
+    os.environ["AWS_SECRET_ACCESS_KEY"] = "testing"
+    os.environ["AWS_SECURIT_TOKEN"] = "testing"
+    os.environ["AWS_SESSION_TOKEN"] = "testing"
+    os.environ["AWS_DEFAULT_REGION"] = "eu-west-2"
+
 
-@pytest.fixture(scope='class')
+@pytest.fixture(scope="class")
 def s3_client(aws_credentials):
     with mock_aws():
-        yield boto3.client('s3')
+        yield boto3.client("s3")
+
+
 class TestReadFromS3:
-    
-    def test_returns_dictionary_with_correct_value_pair(self,s3_client):
-        s3_client.create_bucket(Bucket = 'dummy_buc',CreateBucketConfiguration={
-                                    'LocationConstraint': 'eu-west-2'
-                                })
-        s3_client.upload_file('tests/dummy_identical.csv', 'dummy_buc', 'Foods/2024/08/21/Foods_12:03:10.csv')
-        tables = ['Foods']
-        result = read_from_s3_subfolder_to_df(tables,bucket='dummy_buc',client=s3_client)
+    def test_returns_dictionary_with_correct_value_pair(self, s3_client):
+        s3_client.create_bucket(
+            Bucket="dummy_buc",
+            CreateBucketConfiguration={"LocationConstraint": "eu-west-2"},
+        )
+        s3_client.upload_file(
+            "tests/dummy_identical.csv",
+            "dummy_buc",
+            "Foods/2024/08/21/Foods_12:03:10.csv",
+        )
+        tables = ["Foods"]
+        result = read_from_s3_subfolder_to_df(
+            tables, bucket="dummy_buc", client=s3_client
+        )
         print(result)
-        expected_df =  pd.DataFrame(np.array([['Vegetable', 'Sour', 'Green'], ['Berry', 'Sweet', 'Red']]),
-                   columns=['Food_type', 'Flavour', 'Colour'])
-        assert isinstance(result,dict)
-        assert list(result.keys())[0] == 'Foods'
-        assert isinstance(result['Foods'],pd.DataFrame)
-        assert result['Foods'].eq(expected_df,axis='columns').all(axis=None)
-    
-    def test_returns_dictionary_of_dataframes_for_multiple_tables(self,s3_client):
-        s3_client.upload_file('tests/dummy_2.csv', 'dummy_buc', 'Cars/2024/08/21/Cars_14:03:56.csv')
-        tables = ['Foods','Cars']
-        result = read_from_s3_subfolder_to_df(tables,bucket='dummy_buc',client=s3_client)
-        expected_foods_df =  pd.DataFrame(np.array([['Vegetable', 'Sour', 'Green'], ['Berry', 'Sweet', 'Red']]),
-                   columns=['Food_type', 'Flavour', 'Colour'])
-        expected_cars_df = pd.DataFrame(np.array([['Truck', 'Chevrolet', 'Grey'], ['Convertible', 'Mercedes','Red'],['Van','Volkswagen','Blue']]),
-                   columns=['Car_type', 'Brand', 'Colour'])
-        assert list(result.keys()) == tables
-        assert result['Foods'].eq(expected_foods_df,axis='columns').all(axis=None)
-        assert result['Cars'].eq(expected_cars_df,axis='columns').all(axis=None)
-
+        expected_df = pd.DataFrame(
+            np.array([["Vegetable", "Sour", "Green"], ["Berry", "Sweet", "Red"]]),
+            columns=["Food_type", "Flavour", "Colour"],
+        )
+        assert isinstance(result, dict)
+        assert list(result.keys())[0] == "Foods"
+        assert isinstance(result["Foods"], pd.DataFrame)
+        assert result["Foods"].eq(expected_df, axis="columns").all(axis=None)
 
+    def test_returns_dictionary_of_dataframes_for_multiple_tables(self, s3_client):
+        s3_client.upload_file(
+            "tests/dummy_2.csv", "dummy_buc", "Cars/2024/08/21/Cars_14:03:56.csv"
+        )
+        tables = ["Foods", "Cars"]
+        result = read_from_s3_subfolder_to_df(
+            tables, bucket="dummy_buc", client=s3_client
+        )
+        expected_foods_df = pd.DataFrame(
+            np.array([["Vegetable", "Sour", "Green"], ["Berry", "Sweet", "Red"]]),
+            columns=["Food_type", "Flavour", "Colour"],
+        )
+        expected_cars_df = pd.DataFrame(
+            np.array(
+                [
+                    ["Truck", "Chevrolet", "Grey"],
+                    ["Convertible", "Mercedes", "Red"],
+                    ["Van", "Volkswagen", "Blue"],
+                ]
+            ),
+            columns=["Car_type", "Brand", "Colour"],
+        )
+        assert list(result.keys()) == tables
+        assert result["Foods"].eq(expected_foods_df, axis="columns").all(axis=None)
+        assert result["Cars"].eq(expected_cars_df, axis="columns").all(axis=None)