NumPy is widely used for Scientific computing in Physics, Chemistry, Biology, Astronomy, data analysis, machine learning, Natural Language Processing, and many more disciplines.
NumPy integrates seamlessly with other popular Python libraries such as SciPy, Pandas, and Matplotlib, facilitating streamlined workflows in data analysis and scientific computing.
As an example, NumPy has applications in Physics for Modeling physical systems and simulations. In Biology, Numpy has applications in Data Analysis and bio-informatics.
The next few pages show a few examples of how NumPy can be used in various scientific computing.
Physics
- Atmospheric pressure exponential decay with altitude
- 2-dimension kinematics (projectile)
- Newton’s Universal Law of Gravitation
Chemistry
- Nuclear Chemistry: radioactive isotope decay with time illustrating the concept of half-life
- Relation between pH and H+ Ion Concentration
Finance
- Compound interest calculation at different interest rates
The following pages use real-world problems in high-school level physics, chemistry, and, math to illustrate these concepts through programming using Numpy. The programs provide visualization of the programs output through Matplotlib visualization software. These programming examples introduce the reader to these concepts from the programming angle.
