# P4D-Data-Sciences

**Repository Path**: chen3boy/P4D-Data-Sciences

## Basic Information

- **Project Name**: P4D-Data-Sciences
- **Description**: A collection of lightweight Python wrappers based on Python4Delphi simplifying Data Sciences development with Delphi
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-03-19
- **Last Updated**: 2026-03-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# P4D-Data-Sciences
A collection of lightweight Python wrappers based on Python4Delphi simplifying Data Sciences development with Delphi

This is an _early access preview_, but you are encouraged to try it out, file bug reports, and add features. [Read more](https://blogs.embarcadero.com/?p=145025) and catch the live stream.

## Currently includes:
* **Tensorflow** - Library for machine learning and artificial intelligence. 
* **NumPy** - [[Demos](https://github.com/Embarcadero/P4D-Data-Sciences/tree/main/demos/NumPy)] Numerical library for working with large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these them. 
* **PyTorch & PyTorch Vision** - [[Demos](https://github.com/Embarcadero/P4D-Data-Sciences/tree/main/demos/PyTorch)] For computer vision and natural language processing applications.
* **MatPlotLib** - [[Demos](https://github.com/Embarcadero/P4D-Data-Sciences/tree/main/demos/MatplotLib)] Library for creating static, animated, and interactive visualizations.
* **Natural Language Toolkit (NLTK)** - [Demos](https://github.com/Embarcadero/P4D-Data-Sciences/tree/main/demos/NLTK) Suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English.
* **OpenCV** - Library of programming functions mainly aimed at real-time computer vision
* **Scikit-learn** - [[Demos](https://github.com/Embarcadero/P4D-Data-Sciences/tree/main/demos/ScikitLearn)] Machine learning library that features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN.

Most packages have basic samples, but PyTorch has a very involed [Transfer Learning demonstration](https://github.com/Embarcadero/P4D-Data-Sciences/tree/main/demos/PyTorch/PyTorchTransferLearning).

## New Packages:

* [Keras](https://keras.io/) - Deep learning for humans: Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.
* [ONNXRuntime](https://github.com/microsoft/onnxruntime) - cross-platform, high performance ML inferencing and training accelerator by Microsoft.
* [Pandas](https://pandas.pydata.org/) - powerful Python data analysis toolkit

## Other Python Packages for Delphi
Check out the [PythonPackages4Delphi](https://github.com/Embarcadero/PythonPackages4Delphi) for other non-Data Science related packages.

## Dependencies:

Please make sure the following are installed **_before_** installing this library **_and in the following order:_**  

* [Python4Delphi](https://github.com/Embarcadero/python4delphi)
* [PythonEnvironments](https://github.com/Embarcadero/PythonEnvironments)
* [Lightweight-Python-Wrappers](https://github.com/Embarcadero/Lightweight-Python-Wrappers)

Each library depends on the one above it, once these are installed this library will also install