Statistics has long been a field of mathematics that is relevant to practically all applied disciplines of science and engineering, as well as business, medicine, and other fields where data is used for obtaining knowledge and making decisions. With the recent proliferation of data analytics, there has been a surge of renewed interest in statistical methods. Still, computer-aided statistics has a long history, and it is a field that traditionally has been dominated by domain-specific software packages and programming environments, such as the S language, and more recently its open source counterpart: the R language. The use of Python for statistical analysis has grown rapidly over the last several years, and by now there is a mature collection of statistical libraries for Python. With these libraries Python can match the performance and features of domain-specific languages in many areas of statistics, albeit not all, while also providing the unique advantages of the Python programming language and its environment. The pandas library that we discussed in Chapter 12 is an example of a development within the Python community that was strongly influenced by statistical software, with the introduction of the data frame data structure to the Python environment. The NumPy and SciPy libraries provide computational tools for many fundamental statistical concepts, and higher-level statistical modeling and machine learning are covered by the statsmodels and scikit-learn libraries, which we will see more of in the following chapters.