Add top scientific libraries

[lolpack]

numpy
scipy
pandas
scikit-learn
tensorflow
torch
xgboost
matplotlib
seaborn
plotly
sympy
statsmodels
dask
joblib
opencv-python
scikit-image

Here are some of the top Python scientific packages widely used in research, data science, and engineering:

General Scientific Computing

1. NumPy – Efficient handling of large, multi-dimensional arrays and matrices, along with mathematical operations.
2. SciPy – A library for scientific computing with modules for optimization, integration, interpolation, signal processing, and more.

Data Handling & Analysis

3. pandas – Provides high-performance data structures (Series, DataFrame) for data manipulation and analysis.

Machine Learning & AI

4. scikit-learn – A comprehensive machine learning library with tools for classification, regression, clustering, dimensionality reduction, and more.
5. TensorFlow – A powerful deep learning framework developed by Google.
6. PyTorch – A dynamic deep learning framework popular for research and production.
7. XGBoost – An optimized gradient boosting library for machine learning.

Data Visualization

8. Matplotlib – The fundamental library for creating static, animated, and interactive plots.
9. Seaborn – Built on Matplotlib, it provides a high-level interface for statistical graphics.
10. Plotly – An interactive plotting library, great for dashboards and web applications.

Symbolic Mathematics

11. SymPy – A library for symbolic mathematics, including algebra, calculus, equation solving, and more.

Statistical Analysis

12. statsmodels – Provides statistical models and hypothesis testing tools.

Scientific Workflows & Parallel Computing

13. Dask – Enables parallel computing for large-scale data analysis and NumPy-like operations.
14. Joblib – Optimized for parallel processing and caching of computation results.

Signal Processing & Image Analysis

15. OpenCV (cv2) – Computer vision and image processing library.
16. scikit-image – Image processing tools built on NumPy and SciPy.