IPython Interactive Computing and Visualization Cookbook（Second Edition）

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IPython Interactive Computing and Visualization CookbookSecond Edition

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Preface

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What this book covers

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Sections

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Chapter 1. A Tour of Interactive Computing with Jupyter and IPython

Introduction

Introducing IPython and the Jupyter Notebook

Getting started with exploratory data analysis in the Jupyter Notebook

Introducing the multidimensional array in NumPy for fast array computations

Creating an IPython extension with custom magic commands

Mastering IPython's configuration system

Creating a simple kernel for Jupyter

Chapter 2. Best Practices in Interactive Computing

Introduction

Learning the basics of the Unix shell

Using the latest features of Python 3

Learning the basics of the distributed version control system Git

A typical workflow with Git branching

Efficient interactive computing workflows with IPython

Ten tips for conducting reproducible interactive computing experiments

Writing high-quality Python code

Writing unit tests with pytest

Debugging code with IPython

Chapter 3. Mastering the Jupyter Notebook

Introduction

Teaching programming in the Notebook with IPython Blocks

Converting a Jupyter notebook to other formats with nbconvert

Mastering widgets in the Jupyter Notebook

Creating custom Jupyter Notebook widgets in Python HTML and JavaScript

Configuring the Jupyter Notebook

Introducing JupyterLab

Chapter 4. Profiling and Optimization

Introduction

Evaluating the time taken by a command in IPython

Profiling your code easily with cProfile and IPython

Profiling your code line-by-line with line_profiler

Profiling the memory usage of your code with memory_profiler

Understanding the internals of NumPy to avoid unnecessary array copying

Using stride tricks with NumPy

Implementing an efficient rolling average algorithm with stride tricks

Processing large NumPy arrays with memory mapping

Manipulating large arrays with HDF5

Chapter 5. High-Performance Computing

Introduction

Using Python to write faster code

Accelerating pure Python code with Numba and Just-In-Time compilation

Accelerating array computations with NumExpr

Wrapping a C library in Python with ctypes

Accelerating Python code with Cython

Optimizing Cython code by writing less Python and more C

Releasing the GIL to take advantage of multi-core processors with Cython and OpenMP

Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA

Distributing Python code across multiple cores with IPython

Interacting with asynchronous parallel tasks in IPython

Performing out-of-core computations on large arrays with Dask

Trying the Julia programming language in the Jupyter Notebook

Chapter 6. Data Visualization

Introduction

Using Matplotlib styles

Creating statistical plots easily with seaborn

Creating interactive web visualizations with Bokeh and HoloViews

Visualizing a NetworkX graph in the Notebook with D3.js

Discovering interactive visualization libraries in the Notebook

Creating plots with Altair and the Vega-Lite specification

Chapter 7. Statistical Data Analysis

Introduction

Exploring a dataset with pandas and Matplotlib

Getting started with statistical hypothesis testing — a simple z-test

Getting started with Bayesian methods

Estimating the correlation between two variables with a contingency table and a chi-squared test

Fitting a probability distribution to data with the maximum likelihood method

Estimating a probability distribution nonparametrically with a kernel density estimation

Fitting a Bayesian model by sampling from a posterior distribution with a Markov chain Monte Carlo method

Analyzing data with the R programming language in the Jupyter Notebook

Chapter 8. Machine Learning

Introduction

Getting started with scikit-learn

Predicting who will survive on the Titanic with logistic regression

Learning to recognize handwritten digits with a K-nearest neighbors classifier

Learning from text – Naive Bayes for Natural Language Processing

Using support vector machines for classification tasks

Using a random forest to select important features for regression

Reducing the dimensionality of a dataset with a principal component analysis

Detecting hidden structures in a dataset with clustering

Chapter 9. Numerical Optimization

Introduction

Finding the root of a mathematical function

Minimizing a mathematical function

Fitting a function to data with nonlinear least squares

Finding the equilibrium state of a physical system by minimizing its potential energy

Chapter 10. Signal Processing