dscience-tools 1.0.0

DSTools: Data Science Tools Library

DSTools: Data Science Tools Library

Authors

• @sergiikavun

DSTools is a Python library designed to assist data scientists and researchers by providing a collection of helpful functions for various stages of a data science project, from data exploration and preprocessing to model evaluation and synthetic data generation.

Table of Contents

• Features
• Installation
• Usage
• Function Overview
• Example
• Contributing
• References
• License

Features

• Data Exploration: Quickly get statistics for numerical and categorical features (describe_numeric, describe_categorical), check for missing values (check_NINF), and visualize correlations (corr_matrix).
• Model Evaluation: Comprehensive classification model evaluation (evaluate_classification, compute_metrics) with clear visualizations (plot_confusion_matrix).
• Data Preprocessing: Encode categorical variables (labeling), handle outliers (remove_outliers_iqr), and scale features (min_max_scale).
• Time Series Analysis: Test for stationarity using the Dickey-Fuller test (test_stationarity).
• Synthetic Data Generation: Create complex numerical distributions matching specific statistical moments (generate_distribution, generate_distribution_from_metrics).
• Advanced Statistics: Calculate non-parametric correlation (chatterjee_correlation), entropy, and KL-divergence.
• Utilities: Save/load DataFrames to/from ZIP archives, generate random alphanumeric codes, and more.

Installation

Clone the Repository

git clone https://github.com/s-kav/ds_tools.git

Navigate to the Project Directory

cd ds_tools

Install Dependencies

Ensure you have Python version 3.8 or higher and install the required packages:

pip install -r requirements.txt

Usage

Here's a simple example of how to use the library to evaluate a classification model.

import numpy as np
from ds_tools import DSTools

# 1. Initialize the toolkit
tools = DSTools()

# 2. Generate some dummy data
y_true = np.array([0, 1, 1, 0, 1, 0, 0, 1])
y_probs = np.array([0.1, 0.8, 0.6, 0.3, 0.9, 0.2, 0.4, 0.7])

# 3. Get a comprehensive evaluation report
# This will print metrics and show plots for ROC and Precision-Recall curves.
results = tools.evaluate_classification(true_labels=y_true, pred_probs=y_probs)

# The results are also returned as a dictionary
print(f"\nROC AUC Score: {results['roc_auc']:.4f}")

Full code base for other function testing you can find here.

Function Overview

The library provides a wide range of functions. To see a full, formatted list of available tools, you can use the function_list method:

from ds_tools import DSTools

tools = DSTools()
tools.function_list()

Example

Generating a Synthetic Distribution: need to create a dataset with specific statistical properties? generate_distribution_from_metrics can do that.

from ds_tools import DSTools, DistributionConfig

tools = DSTools()

# Define the desired metrics
metrics_config = DistributionConfig(
    mean=1042,
    median=330,
    std=1500,
    min_val=1,
    max_val=120000,
    skewness=13.2,
    kurtosis=245, # Excess kurtosis
    n=10000
)

# Generate the data
generated_data = tools.generate_distribution_from_metrics(n=10000, metrics=metrics_config)

print(f"Generated Mean: {np.mean(generated_data):.2f}")
print(f"Generated Std: {np.std(generated_data):.2f}")

Full code base for other function testing you can find here.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue on the GitHub repository.

To contribute:

Fork the repository. Create a new branch for your feature or bugfix. Commit your changes with clear messages. Push to your fork and submit a pull request. Please ensure your code adheres to PEP8 standards and includes appropriate docstrings and comments.

References

For citing you should use:

Sergii Kavun. (2025). s-kav/ds_tools: Version 0.9.1 (v0.9.1). Zenodo. https://doi.org/10.5281/zenodo.15864146

License

This project is licensed under the MIT License - see the LICENSE file for details.