Essential Python Packages for Data Analysis, Statistics, and Machine Learning

Python offers a rich ecosystem of packages for data acquisition, cleaning, modeling, and visualization.

NumPy : provides array support.

SciPy : offers matrix support and numerical computation, optimization, and statistics modules.

Pandas : powerful and flexible data analysis and exploration tool.

Matplotlib : robust data visualization library.

StatsModels : statistical modeling and econometrics, including descriptive statistics, model estimation, and inference.

Scikit-Learn : extensive machine‑learning library supporting regression, classification, clustering, etc.

Keras : deep‑learning library for building neural networks and deep models.

Gensim : library for text topic modeling and text mining.

Pillow : image processing.

OpenCV : video processing.

GMPY2 : high‑precision arithmetic.

Statsmodels Package

Statsmodels is a Python tool for statistical modeling and econometrics, complementing SciPy’s statistical functions. Its main features include linear regression models (generalized least squares, ordinary least squares), generalized linear models, discrete variable regression via maximum likelihood, robust linear models, time‑series analysis, non‑parametric estimation, datasets, common statistical tests, and I/O utilities for Stata .dta files, ASCII, LaTeX, and HTML output.

Scikit‑Learn Package

Scikit‑Learn provides a unified interface for models: model.fit() trains the model; for supervised models, model.predict() predicts new samples, model.predict_proba() gives class probabilities (e.g., logistic regression), and model.score() evaluates performance. For unsupervised models, model.transform() learns a new basis, and model.fit_transform() learns and applies that basis.

Keras Package

While Scikit‑Learn is powerful, it does not include artificial neural networks. Keras fills this gap, offering deep‑learning capabilities essential for tasks such as natural language processing and image recognition.

References

朱顺泉, Economic and Financial Data Analysis and Its Python Applications