实例介绍
【实例简介】python语言交互式计算和数据可视化
【实例截图】
【核心代码】
Table of Contents Preface vii Chapter 1: Getting Started with IPython 1 What are Python, IPython, and Jupyter? 1 Jupyter and IPython 2 What this book covers 4 References 5 Installing Python with Anaconda 5 Downloading Anaconda 6 Installing Anaconda 6 Before you get started... 7 Opening a terminal 7 Finding your home directory 8 Manipulating your system path 8 Testing your installation 9 Managing environments 9 Common conda commands 10 References 11 Downloading the notebooks 12 Introducing the Notebook 13 Launching the IPython console 13 Launching the Jupyter Notebook 14 The Notebook dashboard 15 The Notebook user interface 16 Structure of a notebook cell 16 Markdown cells 17 Code cells 18 Table of Contents [ ii ] The Notebook modal interface 19 Keyboard shortcuts available in both modes 19 Keyboard shortcuts available in the edit mode 19 Keyboard shortcuts available in the command mode 20 References 20 A crash course on Python 20 Hello world 21 Variables 21 String escaping 23 Lists 24 Loops 26 Indentation 27 Conditional branches 27 Functions 28 Positional and keyword arguments 29 Passage by assignment 30 Errors 31 Object-oriented programming 32 Functional programming 34 Python 2 and 3 35 Going beyond the basics 36 Ten Jupyter/IPython essentials 37 Using IPython as an extended shell 37 Learning magic commands 42 Mastering tab completion 45 Writing interactive documents in the Notebook with Markdown 47 Creating interactive widgets in the Notebook 49 Running Python scripts from IPython 51 Introspecting Python objects 53 Debugging Python code 54 Benchmarking Python code 55 Profiling Python code 56 Summary 58 Chapter 2: Interactive Data Analysis with pandas 59 Exploring a dataset in the Notebook 59 Provenance of the data 60 Downloading and loading a dataset 61 Making plots with matplotlib 63 Descriptive statistics with pandas and seaborn 67 Table of Contents [ iii ] Manipulating data 69 Selecting data 69 Selecting columns 70 Selecting rows 70 Filtering with boolean indexing 72 Computing with numbers 73 Working with text 75 Working with dates and times 76 Handling missing data 77 Complex operations 78 Group-by 78 Joins 80 Summary 83 Chapter 3: Numerical Computing with NumPy 85 A primer to vector computing 85 Multidimensional arrays 86 The ndarray 86 Vector operations on ndarrays 87 How fast are vector computations in NumPy? 88 How an ndarray is stored in memory 89 Why operations on ndarrays are fast 91 Creating and loading arrays 91 Creating arrays 91 Loading arrays from files 93 Basic array manipulations 94 Computing with NumPy arrays 97 Selection and indexing 98 Boolean operations on arrays 99 Mathematical operations on arrays 100 A density map with NumPy 103 Other topics 107 Summary 108 Chapter 4: Interactive Plotting and Graphical Interfaces 109 Choosing a plotting backend 109 Inline plots 109 Exported figures 111 GUI toolkits 111 Dynamic inline plots 113 Web-based visualization 114 Table of Contents [ iv ] matplotlib and seaborn essentials 115 Common plots with matplotlib 116 Customizing matplotlib figures 120 Interacting with matplotlib figures in the Notebook 122 High-level plotting with seaborn 124 Image processing 126 Further plotting and visualization libraries 129 High-level plotting 129 Bokeh 130 Vincent and Vega 130 Plotly 131 Maps and geometry 132 The matplotlib Basemap toolkit 132 GeoPandas 133 Leaflet wrappers: folium and mplleaflet 134 3D visualization 134 Mayavi 134 VisPy 135 Summary 135 Chapter 5: High-Performance and Parallel Computing 137 Accelerating Python code with Numba 138 Random walk 138 Universal functions 141 Writing C in Python with Cython 143 Installing Cython and a C compiler for Python 143 Implementing the Eratosthenes Sieve in Python and Cython 144 Distributing tasks on several cores with IPython.parallel 148 Direct interface 149 Load-balanced interface 150 Further high-performance computing techniques 153 MPI 153 Distributed computing 153 C/C with Python 154 GPU computing 154 PyPy 155 Julia 155 Summary 155 Table of Contents [ v ] Chapter 6: Customizing IPython 157 Creating a custom magic command in an IPython extension 157 Writing a new Jupyter kernel 160 Displaying rich HTML elements in the Notebook 165 Displaying SVG in the Notebook 165 JavaScript and D3 in the Notebook 167 Customizing the Notebook interface with JavaScript 170 Summary 172 Index 173
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