Python has quickly gone up the ranks to become the most sought-after language for statistics and data science. What makes it so special is that it is a high-level, object-oriented language, all the while being easy to code. We also have a thriving open-source Python community that keeps developing various unique libraries for maths, data analysis, mining, exploration, and visualization.
Keeping that in mind, here are some of the best Python libraries helpful for implementing statistical data. Pandas is a high-performance Python package with easy-to-grasp and expressive data structures. It is designed for rapid data manipulation and visualization and is the best tool when it comes to data munging or wrangling. With this 30k stars+ Github repository, you also get time series-specific functionality. Seaborn is essentially an extension of the Matplotlib plotting library with various advanced features and shorter syntax. With Seaborn, you can determine relationships between various variables, observe and determine aggregate statistics, and plot high-level and multi-plot grids. We also have Prophet, which is a forecasting procedure developed using Python and R. It’s quick and offers automated forecasts for time series data to be used by analysts.
Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)
Python 15941 Version:v1.1.4 License: Permissive (MIT)
Python 5993 Version:v3.11.4 License: Others (Non-SPDX)
Python 6346 Version:0.10.0 License: Permissive (MIT)
Python 2322 Version:Current License: Strong Copyleft (GPL-3.0)
Python 2901 Version:v0.21.0 License: Others (Non-SPDX)
Python 1356 Version:v2.0.3 License: Permissive (MIT)