Python Data Frameworks
Python has established itself as the de facto standard language for data science and machine learning. With its rich library ecosystem, intuitive syntax, and strong community support, it is widely used from data analysis to cutting-edge AI development.
Ecosystem Hierarchy
1. Foundation Layer
- NumPy: Numerical computing foundation, multi-dimensional array operations
- pandas: Core tool for data manipulation and analysis
- SciPy: Comprehensive library for scientific computing
2. Machine Learning Layer
- scikit-learn: Standard implementation for traditional machine learning
- XGBoost/LightGBM: High-performance gradient boosting implementations
- statsmodels: Statistical modeling and testing
3. Deep Learning Layer
- PyTorch: Forefront of research and development, dynamic computation graphs
- TensorFlow: Strong in production environments, static computation graphs
- Keras: High-level API, beginner-friendly
2025 Trends
PyTorch's Advancement
- Achieving 55% share in research field
- De facto standard for generative AI and LLM development
- Ecosystem expansion through Hugging Face integration
Evolution of Large-Scale Data Processing
- PyArrow integration in pandas 2.x
- Enhanced cooperation with Polars and DuckDB
- Integration with distributed processing frameworks
MLOps Maturity
- Standardization of model management and deployment
- Automated pipeline construction
- Enhanced enterprise support
Framework Selection Guidelines
Data Analysis and Preprocessing
- pandas: Standard data manipulation, medium-scale data
- Polars: When high-speed processing needed, large-scale data
- Dask: Distributed processing, data that doesn't fit in memory
Machine Learning
- scikit-learn: Traditional ML, prototyping
- PyTorch: Research and development, implementing latest methods
- TensorFlow: Production environments, edge device deployment
Specialized Fields
- Time Series Analysis: Prophet, statsforecast
- Natural Language Processing: Transformers, spaCy
- Computer Vision: torchvision, OpenCV
Best Practices for Success
Environment Management
- Utilize virtual environments (venv, conda)
- Clear dependency specifications (requirements.txt, poetry)
- Build reproducible experimental environments
Performance Optimization
- Leverage vectorized operations
- Choose appropriate data types
- Consider GPU utilization
Code Quality
- Utilize type hints
- Implement unit tests
- Comprehensive documentation
GitHub Star Comparison
| No | Name | GitHub Stars | Description | Trend | License | Official Site |
|---|---|---|---|---|---|---|
| 1 | TensorFlow | ⭐ 185.8k | Open-source machine learning platform developed by Google. Excels in production deployment with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js for multi-platform support. Features optimization through static computation graphs. | Maintains stable position with 35% share in production deployment in 2025. TensorFlow 2.15 completes Keras integration and enhances edge device support. Reliability and scalability in enterprise large-scale ML systems are highly valued. | Apache 2.0 | Official |
| 2 | PyTorch | ⭐ 84.6k | Dynamic deep learning framework developed by Meta (formerly Facebook). Features dynamic computation graphs, Python-first design, and intuitive APIs. Overwhelmingly supported in research fields and standardly used for academic paper implementations. | Reversed TensorFlow in research field achieving 55% share in 2025. PyTorch 2.0 compile functionality significantly improves production performance. Rapid expansion in generative AI and LLM development with standardized Hugging Face integration. | BSD-3-Clause | Official |
| 3 | Keras | ⭐ 61.9k | High-level deep learning API. Integrated into TensorFlow 2.0+ providing intuitive and user-friendly interface. Supports from prototyping to production use, widely used from beginners to experts. | Established as standard tool for deep learning introduction through completed TensorFlow integration in 2025. Keras 3.0 MultiBackend support (TensorFlow, PyTorch, JAX) significantly improves portability between frameworks. Particularly expanding adoption in educational fields. | Apache 2.0 | Official |
| 4 | scikit-learn | ⭐ 59.8k | Machine learning library for Python. Provides wide range of algorithms for classification, regression, clustering, and dimensionality reduction. Features simple and consistent API, excellent documentation, optimal for educational use. Standard implementation of traditional ML methods. | Maintains position as absolute standard for traditional machine learning in 2025. v1.5 introduces experimental GPU support and enhanced AutoML features. Continues as indispensable tool for deep learning preprocessing, feature engineering, and model evaluation. | BSD-3-Clause | Official |
| 5 | pandas | ⭐ 46.0k | Essential library for Python data analysis. Structured data manipulation with DataFrame, support for various data sources like CSV/JSON/SQL. Foundation tool for data cleaning, transformation, aggregation, and visualization. | Maintains unshakeable position as core of Python data science in 2025. pandas 2.2 brings 50% memory efficiency improvement through PyArrow integration and enhanced large-scale data processing capabilities. Despite Polars competition, maintains advantage through rich ecosystem. | BSD-3-Clause | Official |
| 6 | NumPy | ⭐ 29.9k | Foundation library for Python scientific computing. Provides multi-dimensional arrays, linear algebra, Fourier transforms, and random number generation. Functions as dependency for almost all Python scientific computing libraries, achieving C-level high-speed computation. | Continues as absolute foundation of Python scientific computing ecosystem in 2025. NumPy 2.0 improves ABI compatibility and begins GPU support consideration. Maintains stable growth as essential dependency across all fields of machine learning, data science, and scientific computing. | BSD-3-Clause | Official |