IPython Interactive Computing and Visualization Cookbook - Second Edition: Over 100 hands-on recipes to sharpen your skills in high-performance ... and data science in the Jupyter Notebook

Learn to use IPython and Jupyter Notebook for your data analysis and visualization work.

Key Features

• 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

Book Description

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.

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

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.

Table of Contents

1. A Tour of Interactive Computing with Jupyter and IPython
2. Best Practices in Interactive Computing
3. Mastering the Jupyter Notebook
4. Profiling and Optimization
5. High-Performance Computing
6. Data Visualization
7. Statistical Data Analysis
8. Machine Learning
9. Numerical Optimization
10. Signal Processing
11. Image and Audio Processing
12. Deterministic Dynamical Systems
13. Stochastic Dynamical Systems
14. Graphs, Geometry, and Geographic Information Systems
15. Symbolic and Numerical Mathematics

學習使用IPython和Jupyter Notebook進行數據分析和可視化工作。

主要特點：
- 利用Jupyter Notebook進行交互式數據科學和可視化
- 成為高性能計算和數據分析以及科學建模的專家
- 通過許多實例驅動的手把手解釋，全面涵蓋科學計算

書籍描述：
Python是領先的開源平台之一，用於數據科學和數值計算。IPython和相關的Jupyter Notebook提供了高效的Python接口，用於數據分析和交互式可視化，並且它們是進入該平台的理想入口。

《IPython互動計算和可視化食譜，第二版》包含許多現成的、專注的食譜，用於高性能科學計算和數據分析，從最新的IPython/Jupyter功能到最高級的技巧，幫助您編寫更好、更快的代碼。您將應用這些最先進的方法到各種實際例子中，展示應用數學、科學建模和機器學習的主題。

本書的第一部分涵蓋了編程技術：代碼質量和可重現性、代碼優化、通過即時編譯實現高性能計算、並行計算和圖形卡編程。第二部分涉及數據科學、統計學、機器學習、信號和圖像處理、動態系統以及純數學和應用數學。

您將學到：
- 掌握Jupyter Notebook的所有功能
- 編寫更好的代碼：編寫高質量、可讀性強、經過良好測試的程序；對代碼進行性能分析和優化；進行可重現的交互式計算實驗
- 在Jupyter Notebook中可視化數據並創建交互式圖表
- 使用NumPy、ctypes、Numba、Cython、OpenMP、GPU編程（CUDA）、並行IPython、Dask等快速編寫Python程序
- 使用貝葉斯或頻率統計（Pandas、PyMC和R）分析數據，並從實際數據中學習機器學習（scikit-learn）
- 使用SciPy、scikit-image和OpenCV獲取信號、圖像和聲音的寶貴洞察
- 在Python中模擬確定性和隨機動態系統
- 使用SymPy和Sage在Python中熟悉數學：代數、分析、邏輯、圖形、幾何和概率論

適合對數值計算和數據科學感興趣的人：學生、研究人員、教師、工程師、分析師和愛好者。建議具備Python/NumPy的基本知識，一些數學技能將有助於理解計算方法的理論。

目錄：
1. Jupyter和IPython的交互式計算之旅
2. 交互式計算的最佳實踐
3. 掌握Jupyter Notebook
4. 性能分析和優化
5. 高性能計算
6. 數據可視化
7. 統計數據分析
8. 機器學習
9. 數值優化
10. 信號處理
11. 圖像和音頻處理
12. 確定性動態系統
13. 隨機動態系統
14. 圖形、幾何和地理信息系統
15. 符號和數值數學