Python Libraries for Probability and Statistics

- Overview

In the era of big data and artificial intelligence, data science and machine learning have become key to many fields of science and technology. An essential aspect of working with data is the ability to describe, summarize and represent the data visually. 

The Python statistics library is a comprehensive, popular, and widely used tool to assist you in processing data.

Following are just a few of the many Python libraries that are available for probability and statistics. The best library for you will depend on your specific needs and requirements. 

• NumPy: NumPy is a powerful Python library for numerical computing, including extensive support for arrays, matrices, and linear algebra. It is often used in conjunction with Pandas for statistical analysis.
• SciPy: SciPy is a Python library that provides a wide range of scientific computing functions, including statistical functions such as probability distributions, hypothesis testing, and statistical modeling.
• Pandas: Pandas is a Python library for data analysis and manipulation. It provides data structures and operations for working with large datasets, including statistical functions such as descriptive statistics and time series analysis.
• Statsmodels: Statsmodels is a Python library for statistical modeling and analysis. It provides support for various types of regression models, time series analysis, and hypothesis testing.
• Matplotlib: Matplotlib is a Python library for data visualization. It can be used to create a variety of plots and charts, including histograms, scatter plots, and line charts.
• Seaborn: Seaborn is a Python library that builds on Matplotlib to provide a high-level interface for creating statistical graphics. It provides a variety of themes and styles for plots, as well as a number of statistical functions that can be used to customize plots.

[More to come ...]