Jupyter Cookbook
图书信息
| 作者 | Dan Toomey |
| 出版社 | Packt Publishing |
| ISBN | 9781788839747 |
| 出版时间 | 2018-04-30 |
| 字数 | 17.3万 |
| 分类 | 进口书,外文原版书,电脑,网络 |
读书简介
Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle About This Book ? Create and share interactive documents with live code, text and visualizations ? Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter ? Develop your widgets and interactive dashboards with these innovative recipes Who This Book Is For This cookbook is for data science professionals, developers, technical data analysts, and programmers who want to execute technical coding, visualize output, and do scientific computing in one tool. Prior understanding of data science concepts will be helpful, but not mandatory, to use this book. What You Will Learn ? Install Jupyter and configure engines for Python, R, Scala and more ? Access and retrieve data on Jupyter Notebooks ? Create interactive visualizations and dashboards for different scenarios ? Convert and share your dynamic codes using HTML, JavaScript, Docker, and more ? Create custom user data interactions using various Jupyter widgets ? Manage user authentication and file permissions ? Interact with Big Data to perform numerical computing and statistical modeling ? Get familiar with Jupyter's next-gen user interface - JupyterLab In Detail Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it. Style and approach The recipes in this book are highly practical and very easy to follow, and include tips and tricks that will help you crack any problem that you might come across while getting the most out of your Jupyter notebook.
目录
Title Page
Copyright and Credits
Jupyter Cookbook
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Installation and Setting up the Environment
Introduction
Installing Jupyter on Windows
Getting ready
How to do it...
Installing Jupyter directly
Installing Jupyter through Anaconda
Installing Jupyter on the Mac
Getting ready
How to do it...
Installing Jupyter on the Mac via Anaconda
Installing Jupyter on the the Mac via the command line
Installing Jupyter on Linux
How to do it...
Installing Jupyter on a server
How to do it...
Example Notebook with a user data collision
Adding an Engine
Introduction
Adding the Python 3 engine
How to do it...
Installing the Python 3 engine
Running a Python 3 script
Adding the R engine
How to do it...
Installing the R engine using Anaconda Navigator
Installing the R engine via command line
Running an R Script
Adding the Julia engine
How to do it...
Installing the Julia engine
Running a Julia script
Adding the JavaScript engine
How to do it...
Installing the Node.JS engine
Running a Node.JS script
Adding the Scala engine
How to do it...
Installing the Scala engine
Running a Scala script
Adding the Spark engine
How to do it...
Installing the Spark engine
Running a Spark script
Accessing and Retrieving Data
Introduction
Reading CSV files
Getting ready
How to do it...
How it works...
Reading JSON files
Getting ready
How to do it...
How it works...
Accessing a database
Getting ready
How to do it...
How it works...
Reading flat files
Getting ready
How to do it...
How it works...
Reading text files
Getting ready
How to do it...
How it works...
Visualizing Your Analytics
Introduction
Generating a line graph using Python
How to do it...
How it works...
Generating a histogram using Python
How to do it...
How it works...
Generating a density map using Python
How to do it...
How it works...
Plotting 3D data using Python
How to do it...
How it works...
Present a user-interactive graphic using Python
How to do it...
How it works...
Visualizing with R
How to do it...
How it works...
Generate a regression line of data using R
How to do it...
How it works...
Generate an R lowess line graph
How to do it...
How it works...
Producing a Scatter plot matrix using R
How to do it...
How it works...
Producing a bar chart using R
How to do it...
How it works...
Producing a word cloud using R
How to do it...
How it works...
Visualizing with Julia
Getting ready
How to do it...
Drawing a Julia scatter diagram of Iris data using Gadfly
How to do it...
Drawing a Julia histogram using Gadfly
How to do it...
How it works...
Drawing a Julia line graph using the Winston package
How to do it...
How it works...
Working with Widgets
Introduction
What are widgets?
Getting ready
How to do it...
How it works...
Using ipyleaflet widgets
Getting ready
How to do it...
How it works...
Using ipywidgets
Getting ready
How to do it...
How it works...
Using a widget container
How to do it...
Using an interactive widget
How to do it...
How it works...
Using an interactive text widget
How to do it...
How it works...
Linking widgets together
How to do it...
How it works...
Another ipywidgets linking example
How to do it...
How it works...
Using a cookie cutter widget
Getting ready
How to do it...
How it works...
Developing an OPENGL widget
Getting ready
Creating a simple orbit of one object
How to do it...
How it works...
Using a complex orbit of multiple objects
How to do it...
How it works...
Jupyter Dashboards
Introduction
What is Jupyter dashboards?
Getting ready
How to do it...
There's more...
Creating an R dashboard
How to do it...
How it works...
Create a Python dashboard
How to do it...
Creating a Julia dashboard
How to do it...
Develop a JavaScript (Node.js) dashboard
How to do it...
Sharing Your Code
Introduction
Sharing your Notebook using server software
Using a Notebook server
How to do it...
Using web encryption for your Notebook
Using a web server
How to do it...
Sharing your Notebook through a public server
How to do it...
Sharing your Notebook through Docker
How to do it...
Sharing your Notebook using nbviewer
How to do it...
Converting your Notebook into a different format
How to do it...
Converting Notebooks to R
How to do it...
How it works...
Converting Notebooks to HTML
How to do it...
How it works...
Converting Notebooks to Markdown
How to do it...
How it works...
Converting Notebooks to reStructedText
How to do it...
How it works...
Converting Notebooks to Latex
How to do it...
How it works...
Converting Notebooks to PDF
How to do it...
How it works...
Multiuser Jupyter
Introduction
Why multiuser?
How to do it...
How it works...
Providing multiuser with JupyterHub
Getting ready
How to do it...
Providing multiuser with Docker
Getting ready
How to do it...
Running your Notebook in Google Cloud Platform
Getting ready
Set up your GC project
Create a Cloud storage bucket
Create a cluster
Install Jupyter
Download the script
Execute the script
Configure Jupyter
How to do it...
Next steps
There's more...
Running your Notebook in AWS
Getting ready
How to do it...
How it works...
There's more...
Running your Notebook in Azure
Getting ready
How to do it...
How it works...
There's more...
Interacting with Big Data
Introduction
Obtaining a word count from a big-text data source
How to do it...
How it works...
Obtaining a sorted word count from a big-text source
How to do it...
How it works...
Examining big-text log file access
How to do it...
How it works...
Computing prime numbers using parallel operations
How to do it...
How it works...
Analyzing big-text data
How to do it...
How it works...
Analyzing big data history files
How to do it...
How it works...
Jupyter Security
Introduction
How much risk?
Known vulnerabilities
Web attack strategies
Inherent Jupyter security issues
Security mechanisms built into Jupyter
How to do it...
Token-based authentication
Password authentication
No authentication
Using SSL
How to do it...
Creating an SSL certificate
Apply the SSL certificate
The Jupyter trust model
How to do it...
Trust overrides
Collaboration
Controlling network access
How to do it...
Controlling domain access
Controlling IP access
Additional practices
How to do it...
Server IP address
URL prefix
No browser
Jupyter Labs
Introduction
JupyterLab features
Installing and starting JupyterLab
How to do it...
Installing JupyterLab
Starting JupyterLab
JupyterLab display
How to do it...
JupyterLab menus
How to do it...
Starting a Notebook
How to do it...
Starting a console
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