Book description
Learn to use IPython and Jupyter Notebook for your data analysis and visualization work.
About This Book
- Leverage the Jupyter Notebook for interactive data science and visualization
- Become an expert in high-performance computing and visualization for data analysis and scientific modeling
- A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations
Who This Book Is For
This book is intended for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
What You Will Learn
- Master all features of the Jupyter Notebook
- Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments
- Visualize data and create interactive plots in the Jupyter Notebook
- Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more
- Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn)
- Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV
- Simulate deterministic and stochastic dynamical systems in Python
- Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory
In Detail
Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform.
IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning.
The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.
Style and approach
IPython Interactive Computing and Visualization Cookbook, Second Edition is a practical, hands-on book that will teach you how to analyze and visualize all kinds of data in the Jupyter Notebook.
Table of contents
-
IPython Interactive Computing and Visualization Cookbook Second Edition
- Table of Contents
- IPython Interactive Computing and Visualization CookbookSecond Edition
- Contributors
- Preface
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
- 9. Numerical Optimization
- 10. Signal Processing
- 11. Image and Audio Processing
- 12. Deterministic Dynamical Systems
- 13. Stochastic Dynamical Systems
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14. Graphs, Geometry, and Geographic Information Systems
- Introduction
- Manipulating and visualizing graphs with NetworkX
- Drawing flight routes with NetworkX
- Resolving dependencies in a directed acyclic graph with a topological sort
- Computing connected components in an image
- Computing the Voronoi diagram of a set of points
- Manipulating geospatial data with Cartopy
- Creating a route planner for a road network
-
15. Symbolic and Numerical Mathematics
- Introduction
- Diving into symbolic computing with SymPy
- Solving equations and inequalities
- Analyzing real-valued functions
- Computing exact probabilities and manipulating random variables
- A bit of number theory with SymPy
- Finding a Boolean propositional formula from a truth table
- Analyzing a nonlinear differential system — Lotka-Volterra (predator-prey) equations
- Getting started with Sage
- Index
Product information
- Title: IPython Interactive Computing and Visualization Cookbook - Second Edition
- Author(s):
- Release date: January 2018
- Publisher(s): Packt Publishing
- ISBN: 9781785888632
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