Featured recipes

The IPython Cookbook contains more than 100 recipes on numerical computing and data science with Python. The integrality of the code is freely available on GitHub. In addition, I have released several featured recipes so far. These recipes contain not only the code, but also the full recipe description, the explanations, and all references. They cover a wide range of the topics covered in the book. I intend to release more featured recipes in the future.

Here are the six featured recipes available today:

  1. Getting the best performance out of NumPy. This recipe elaborates on a blog post I had written a while ago. It gives a few tricks that can make your NumPy code much more efficient. I also explain a few implementation details of NumPy that you need to know in order to get the best performance out of your code.

    NumPy array layout.

  2. Simulating a physical system by minimizing an energy. This recipe illustrates how to use SciPy's optimization routines to simulate a physical system.

    NumPy array layout.

  3. Creating a route planner for road network. This recipe implements a GPS-like itinerary planner in pure Python! It uses the NetworkX package to compute a shortest path in a road network. The example uses an open dataset with the full road network of California.

    A GPS in Python!

  4. Introduction to machine learning in Python with scikit-learn. This recipe illustrates the most fundamental concepts of machine learning with scikit-learn. This is the recipe for you if you're just getting started with machine learning, and you want to understand the meaning of supervised and unsupervised learning, classification, regression, clustering, feature selection, feature extraction, overfitting, regularization, and cross-validation. This recipe illustrates some of these concepts on a simple curve fitting problem, using least squares and ridge regression.

    Introduction to scikit-learn

  5. Simulating a partial differential equation (PDE): reaction-diffusion systems and Turing patterns. This recipe shows how to simulate a PDE in Python and NumPy, using the finite difference method.

    Turing patterns in Python

  6. Getting started with Vispy. This recipe illustrates the fundamental concepts of OpenGL for big data visualization using Vispy, a Python library I've been working on for nearly two years.

    Vispy examples

  7. Introduction to statistical data analysis in Python – frequentist and Bayesian methods. This recipe introduces the basis of frequentist and Bayesian methods for statistical data analysis, using a simple coin tossing example.

    A z-test

I'm planning to release more featured recipes in the near future. Let me know if there's one recipe in particular you'd like to see among all of the cookbook's recipes.