基本信息

---

下载

   友情提示：（无需注册或充值，赞助后即可获取资源下载链接）

---

     嘿，亲！知识可是无价之宝呢，但咱这精心整理的资料也耗费了不少心血呀。小小地破费一下，绝对物超所值哦！如有下载和支付问题，请联系我们QQ(微信同号)：813200300

本次赞助数额为： 2 元　

支付宝支付

微信支付

   源码介绍

---

python语言交互式计算和数据可视化

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