Images for pytest post

_posts/2024-04-07-unit-testing-datascience.md

@@ -2,9 +2,9 @@
 layout: post
 title: Enhancing Data Science Workflow with Unit Testing
 subtitle: Enhancing Data Science Workflow with Unit Testing
-cover-img: /assets/img/path.jpg
-thumbnail-img: /assets/img/thumb.png
-share-img: /assets/img/path.jpg
+cover-img: /assets/img/pytest_1.jpg
+thumbnail-img: /assets/img/pytest_thumb.png
+share-img: /assets/img/pytest_2.jpg
 gh-repo: arpithub/arpithub.github.io
 gh-badge: [star, fork, follow]
 tags: [datascience,testing,pytest,ml]
@@ -25,8 +25,7 @@ data-science-project/
 ├── src/
 │   ├── preprocessing.py
 │   └── model.py
-│
-├── tests/
+│── tests/
 │   ├── test_preprocessing.py
 │   └── test_model.py
 │
@@ -81,14 +80,19 @@ Now, create `tests/test_preprocessing.py` to write unit tests for the preprocess
 
 ```python
 import pytest
+from pathlib import Path
 from src.preprocessing import load_iris_dataset, preprocess_data
 
+data_dir = Path(__file__).parent.parent / 'data'  # Navigate up to the project root
+iris_path = data_dir / 'iris.csv'
+
+
 @pytest.fixture
 def iris_data():
-    return load_iris_dataset('data/iris.csv')
+    return load_iris_dataset(iris_path)
 
 def test_load_iris_dataset():
-    df = load_iris_dataset('data/iris.csv')
+    df = load_iris_dataset(iris_path)
     assert not df.empty
 
 def test_preprocess_data(iris_data):
@@ -97,16 +101,16 @@ def test_preprocess_data(iris_data):
     assert 'species' in preprocessed_df.columns
 
 def test_missing_values():
-    df = iris_data()
+    df = load_iris_dataset(iris_path)
     assert not df.isnull().values.any(), "Dataset contains missing values"
 
 def test_no_duplicates():
-    df = iris_data()
+    df = load_iris_dataset(iris_path)
     assert not df.duplicated().any(), "Dataset contains duplicate records"
 
 
 def test_column_datatypes():
-    df = iris_data()
+    df = load_iris_dataset(iris_path)
     expected_datatypes = {
         'sepal length (cm)': 'float64',
         'sepal width (cm)': 'float64',
@@ -145,12 +149,15 @@ Now, create `tests/test_model.py` to write unit tests for the model training and
 
 ```python
 import pytest
+from pathlib import Path
 from src.model import train_and_evaluate_model
 from src.preprocessing import load_iris_dataset, preprocess_data
+data_dir = Path(__file__).parent.parent / 'data'  # Navigate up to the project root
+iris_path = data_dir / 'iris.csv'
 
 @pytest.fixture
 def preprocessed_iris_data():
-    df = load_iris_dataset('data/iris.csv')
+    df = load_iris_dataset(iris_path)
     return preprocess_data(df)
 
 def test_train_and_evaluate_model(preprocessed_iris_data):
@@ -159,7 +166,7 @@ def test_train_and_evaluate_model(preprocessed_iris_data):
 ```
 
 #### Running the Tests
-To run the tests using Pytest, navigate to the project directory and execute:
+To run the tests using Pytest, navigate to the `tests` directory and execute:
 
 ```bash
 pytest