scikit-tools

scikit-tools provides 13 standalone, straight-through Python examples. Each topic file contains its own imports, dataset setup, and analysis code; examples are not hidden inside functions.

Install from a local checkout

python -m pip install -e .

This installs the package and its dependencies. Each topic loads the Iris dataset provided by sklearn.datasets.load_iris, formats its columns to match the examples, and does not download or write a CSV file.

Open or run a topic

python -m scikit_tools.numpy_stats
python -m scikit_tools.pca

You can also open any topic script under scikit_tools directly in your editor, inspect or copy its raw code, and run it with the editor's Python run command. These are scripts: importing one executes its example.

Topics

1. NumPy statistics (numpy_stats.py)
2. Inspecting the Iris dataset (inspect_dataset.py)
3. Handling missing values (missing_values.py)
4. Data transformation (data_transformation.py)
5. Histogram and scatter-plot EDA (eda_plots.py)
6. Box plot and IQR outlier detection (outlier_detection.py)
7. Descriptive statistics (descriptive_stats.py)
8. Correlation and covariance (correlation_covariance.py)
9. Independent-samples t-test (hypothesis_testing.py)
10. Linear regression (linear_regression.py)
11. Logistic-regression classification (logistic_regression.py)
12. K-means clustering (kmeans_clustering.py)
13. PCA and classification (pca.py)

The examples use the Iris columns SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, and Species. The species names use the Iris-setosa style. The missing values example intentionally adds missing values to a DataFrame copy and fills them; it does not change the source dataset.