ABSTRACT
Data analysis is an integral part of life sciences research and is often complex and time-intensive. Computational analysis of the data enables a swifter and more efficient approach to generate data insights. In this chapter, we will cover the applications of Python programming language for biological data handling and analysis. We will introduce the readers to common data formats and data libraries. NumPy - the core library for scientific computing in Python - will be introduced to readers in this chapter. It provides a high-performance multidimensional array object and tools for working with such arrays. The readers are will then be introduced to various matrix operations using NumPy - such as transposing and inversing a matrix, calculating a determinant, matrix multiplication, comparing matrices, and more. The next section of the chapter involves the usage of a powerful and flexible “Pandas” library which is created for data handling and analysis in life sciences areas of concern.
