Basic Statistics in Python

Python provides us with quite a few libraries through which the various statistical concepts explored in the Theory section can be put to use.

For Descriptive Statistics, libraries such as NumPy and Pandas are enough to calculate Measures of Frequency, Central Tendency, Variability and Shape, while Matplotlib helps in creating various graphs. For Inferential Statistics, a range of libraries can be used, with the most important being scipy.stats which allows the user to perform various kinds of t-Tests, F-Tests etc. A number of hypothetical datasets have been used in this section to demonstrate the application of these libraries.

This section also covers the application of Univariate and Bivariate Analysis, which has been covered in the Theory section of 'Data Exploration and Preparation' as it uses the concepts of Descriptive and Inferential statistics only.