Why is Python good for statistics?

Python and R are widely used languages for statistical analysis or machine learning projects. These are the best when it comes to statistic analysis. But there are others – like Java, Scala, or Matlab.

Python’s built-in analytics tools make it a perfect tool for processing complex data . Python’s built-in analytics tools can also easily penetrate patterns, correlate information in extensive sets, and provide better insights, in addition to other critical matrices in evaluating performance.

While both Python and R can accomplish many of the same data tasks, they each have their own unique strengths. … Strengths and weaknesses. Python is better for…R is better for…Performing non-statistical tasks, like web scraping, saving to databases, and running workflowsIts robust ecosystem of statistical packagesPython or R for Data Analysis: Which Should I Learn? | Coursera www.coursera.org › articles › python-or-r-for-data-analysis

Python’s statistics is a built-in Python library for descriptive statistics . You can use it if your datasets are not too large or if you can’t rely on importing other libraries. NumPy is a third-party library for numerical computing, optimized for working with single- and multi-dimensional arrays.