# Welcome to pycse - Python Computations in Science and Engineering

## Contents

# Welcome to pycse - Python Computations in Science and EngineeringÂ¶

pycse is a few different things. First, it is short for *Python computations in science and engineering*. I have written a book on the topic and many blog posts that are described below. It is *also* a Python library that augments numpy and scipy for scientific programming. This functionality is described in some of the book and blog content, and in the documentation linked below. The content here has been in the making for over a decade now. Some of it shows its age, but I hope you still find it useful!

There are three main areas of content here:

The pycse book - these are notes from a course in mathematical modeling I teach at CMU.

The pycse blog - these are from a series of blog posts that I wrote over the past decade

pycse documentation - these are generated from docstrings in the pycse module I wrote.

# Support this workÂ¶

This work is time-consuming and hard. Your support can help me stay motivated to continue and grow it. There are a few ways you can support this work.

Report typos, bugs, and issues at https://github.com/jkitchin/pycse/issues. This will help make the work higher quality over time.

Sponsor me

Buy a book at https://pointbreezepubs.gumroad.com/. This is a newer project than this one, and it is more current.

Tell people about this project so they can learn from it too.

- The pycse book
- Introduction to Python and Jupyter
- More about using Jupyter notebooks
- Integration in Python
- First-order differential equations
- Systems of first-order differential equations
- N
^{th}order differential equations - Nonlinear algebra
- Polynomials in Python
- Boundary value problems
- Introduction to optimization
- Nonlinear regression
- Uncertainty quantification in nonlinear regression
- Constrained optimization
- Introduction to linear algebra
- Applications of linear algebra
- Interpolation
- Linear regression
- Introduction to automatic differentiation
- Introduction to machine learning
- Topics in machine learning
- Gaussian Process Regression
- Concluding remarks

- About pycse
- About the Python packages
- Running pycse
- Documentation
- pycse - Beginner mode
- Avoiding indexing in lists
- functional approach to slicing
- Other pieces of a list
- More user-friendly functions
- Simpler integration
- pycse.utils
- Float comparisons
- Temporarily ignore errors
- Read a Google Sheet into a pandas Dataframe
- Build statistics