Learning IPython for Interactive Computing and Data Visualization - Second Edition

Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book Learn the basics of Python in the Jupyter Notebook Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, Read more... Abstract: Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book Learn the basics of Python in the Jupyter Notebook Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know. What You Will Learn Install Anaconda and code in Python in the Jupyter Notebook Load and explore datasets interactively Perform complex data manipulations effectively with pandas Create engaging data visualizations with matplotlib and seaborn Simulate mathematical models with NumPy Visualize and process images interactively in the Jupyter Notebook with scikit-image Accelerate your code with Numba, Cython, and IPython.parallel Extend the Notebook interface with HTML, JavaScript, and D3 In Detail Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data. Style and approach This is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook. Downloading the example code for thi Cover 1 Copyright 3 Credits 4 About the Author 5 About the Reviewers 6 www.PacktPub.com 7 Table of Contents 8 Preface 14 Chapter 1: Getting Started with IPython 20 What are Python, IPython, and Jupyter? 20 Jupyter and IPython 21 What this book covers 23 References 24 Installing Python with Anaconda 24 Downloading Anaconda 25 Installing Anaconda 25 Before you get started... 26 Opening a terminal 26 Finding your home directory 27 Manipulating your system path 27 Testing your installation 28 Managing environments 28 Common conda commands 29 References 30 Downloading the notebooks 31 Introducing the Notebook 32 Launching the IPython console 32 Launching the Jupyter Notebook 33 The Notebook dashboard 34 The Notebook user interface 35 Structure of a notebook cell 35 Markdown cells 36 Code cells 37 The Notebook modal interface 38 Keyboard shortcuts available in both modes 38 Keyboard shortcuts available in the edit mode 38 Keyboard shortcuts available in the command mode 39 References 39 A crash course on Python 39 Hello world 40 Variables 40 String escaping 42 Lists 43 Loops 45 Indentation 46 Conditional branches 46 Functions 47 Positional and keyword arguments 48 Passage by assignment 49 Errors 50 Object-oriented programming 51 Functional programming 53 Python 2 and 3 54 Going beyond the basics 55 Ten Jupyter/IPython essentials 56 Using IPython as an extended shell 56 Learning magic commands 61 Mastering tab completion 64 Writing interactive documents in the Notebook with Markdown 66 Creating interactive widgets in the Notebook 68 Running Python scripts from IPython 70 Introspecting Python objects 72 Debugging Python code 73 Benchmarking Python code 74 Profiling Python code 75 Summary 77 Chapter 2: Interactive Data Analysis with pandas 78 Exploring a dataset in the Notebook 78 Provenance of the data 79 Downloading and loading a dataset 80 Making plots with matplotlib 82 Descriptive statistics with pandas and seaborn 86 Manipulating data 88 Selecting data 88 Selecting columns 89 Selecting rows 89 Filtering with boolean indexing 91 Computing with numbers 92 Working with text 94 Working with dates and times 95 Handling missing data 96 Complex operations 97 Group-by 97 Joins 99 Summary 102 Chapter 3: Numerical Computing with NumPy 104 A primer to vector computing 104 Multidimensional arrays 105 The ndarray 105 Vector operations on ndarrays 106 How fast are vector computations in NumPy? 107 How an ndarray is stored in memory 108 Why operations on ndarrays are fast 110 Creating and loading arrays 110 Creating arrays 110 Loading arrays from files 112 Basic array manipulations 113 Computing with NumPy arrays 116 Selection and indexing 117 Boolean operations on arrays 118 Mathematical operations on arrays 119 A density map with NumPy 122 Other topics 126 Summary 127 Chapter 4: Interactive Plotting and Graphical Interfaces 128 Choosing a plotting backend 128 Inline plots 128 Exported figures 130 GUI toolkits 130 Dynamic inline plots 132 Web-based visualization 133 matplotlib and seaborn essentials 134 Common plots with matplotlib 135 Customizing matplotlib figures 139 Interacting with matplotlib figures in the Notebook 141 High-level plotting with seaborn 143 Image processing 145 Further plotting and visualization libraries 148 High-level plotting 148 Bokeh 149 Vincent and Vega 149 Plotly 150 Maps and geometry 151 The matplotlib Basemap toolkit 151 GeoPandas 152 Leaflet wrappers: folium and mplleaflet 153 3D visualization 153 Mayavi 153 VisPy 154 Summary 154 Chapter 5: High-Performance and Parallel Computing 156 Accelerating Python code with Numba 157 Random walk 157 Universal functions 160 Writing C in Python with Cython 162 Installing Cython and a C compiler for Python 162 Implementing the Eratosthenes Sieve in Python and Cython 163 Distributing tasks on several cores with IPython.parallel 167 Direct interface 168 Load-balanced interface 169 Further high-performance computing techniques 172 MPI 172 Distributed computing 172 C/C++ with Python 173 GPU computing 173 PyPy 174 Julia 174 Summary 174 Chapter 6: Customizing IPython 176 Creating a custom magic command in an IPython extension 176 Writing a new Jupyter kernel 179 Displaying rich HTML elements in the Notebook 184 Displaying SVG in the Notebook 184 JavaScript and D3 in the Notebook 186 Customizing the Notebook interface with JavaScript 189 Summary 191 Index 192 Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book Learn the basics of Python in the Jupyter Notebook Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know. What You Will Learn Install Anaconda and code in Python in the Jupyter Notebook Load and explore datasets interactively Perform complex data manipulations effectively with pandas Create engaging data visualizations with matplotlib and seaborn Simulate mathematical models with NumPy Visualize and process images interactively in the Jupyter Notebook with scikit-image Accelerate your code with Numba, Cython, and IPython.parallel Extend the Notebook interface with HTML, JavaScript, and D3 In Detail Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data. Style and approach This is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook. Downloading the example code for thi .. Get started with Python for data analysis and numerical computing in the Jupyter NotebookKey FeaturesLearn the basics of Python in the Jupyter NotebookAnalyze and visualize data with pandas, NumPy, matplotlib, and seabornPerform highly-efficient numerical computations with Numba, Cython, and ipyparallelBook DescriptionPython is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.What you will learnInstall Anaconda and code in Python in the Jupyter NotebookLoad and explore datasets interactivelyPerform complex data manipulations effectively with pandasCreate engaging data visualizations with matplotlib and seabornSimulate mathematical models with NumPyVisualize and process images interactively in the Jupyter Notebook with scikit-imageAccelerate your code with Numba, Cython, and IPython.parallelExtend the Notebook interface with HTML, JavaScript, and D3Who this book is forThis book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.

Get started with Python for data analysis and numerical computing in the Jupyter notebook

About This Book

• Learn the basics of Python in the Jupyter Notebook
• Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn
• Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel

Who This Book Is For

This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required