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Create and share livecode, equations, visualizations, and
explanatory text, in both a single document and a web browser with
Jupyter Key Features Learn how to use Jupyter 5.x features such as
cell tagging and attractive table styles Leverage big data tools
and datasets with different Python packages Explore multiple-user
Jupyter Notebook servers Book DescriptionThe Jupyter Notebook
allows you to create and share documents that contain live code,
equations, visualizations, and explanatory text. The Jupyter
Notebook system is extensively used in domains such as data
cleaning and transformation, numerical simulation, statistical
modeling, and machine learning. Learning Jupyter 5 will help you
get to grips with interactive computing using real-world examples.
The book starts with a detailed overview of the Jupyter Notebook
system and its installation in different environments. Next, you
will learn to integrate the Jupyter system with different
programming languages such as R, Python, Java, JavaScript, and
Julia, and explore various versions and packages that are
compatible with the Notebook system. Moving ahead, you will master
interactive widgets and namespaces and work with Jupyter in a
multi-user mode. By the end of this book, you will have used
Jupyter with a big dataset and be able to apply all the
functionalities you've explored throughout the book. You will also
have learned all about the Jupyter Notebook and be able to start
performing data transformation, numerical simulation, and data
visualization. What you will learn Install and run the Jupyter
Notebook system on your machine Implement programming languages
such as R, Python, Julia, and JavaScript with the Jupyter Notebook
Use interactive widgets to manipulate and visualize data in real
time Start sharing your Notebook with colleagues Invite your
colleagues to work with you on the same Notebook Organize your
Notebook using Jupyter namespaces Access big data in Jupyter for
dealing with large datasets using Spark Who this book is
forLearning Jupyter 5 is for developers, data scientists, machine
learning users, and anyone working on data analysis or data science
projects across different teams. Data science professionals will
also find this book useful for performing technical and scientific
computing collaboratively.
Your one-stop guide to building an efficient data science pipeline
using Jupyter About This Book * Get the most out of your Jupyter
notebook to complete the trickiest of tasks in Data Science * Learn
all the tasks in the data science pipeline-from data acquisition to
visualization-and implement them using Jupyter * Get ahead of the
curve by mastering all the applications of Jupyter for data science
with this unique and intuitive guide Who This Book Is For This book
targets students and professionals who wish to master the use of
Jupyter to perform a variety of data science tasks. Some
programming experience with R or Python, and some basic
understanding of Jupyter, is all you need to get started with this
book. What You Will Learn * Understand why Jupyter notebooks are a
perfect fit for your data science tasks * Perform scientific
computing and data analysis tasks with Jupyter * Interpret and
explore different kinds of data visually with charts, histograms,
and more * Extend SQL's capabilities with Jupyter notebooks *
Combine the power of R and Python 3 with Jupyter to create dynamic
notebooks * Create interactive dashboards and dynamic presentations
* Master the best coding practices and deploy your Jupyter
notebooks efficiently In Detail Jupyter Notebook is a web-based
environment that enables interactive computing in notebook
documents. It allows you to create documents that contain live
code, equations, and visualizations. This book is a comprehensive
guide to getting started with data science using the popular
Jupyter notebook. If you are familiar with Jupyter notebook and
want to learn how to use its capabilities to perform various data
science tasks, this is the book for you! From data exploration to
visualization, this book will take you through every step of the
way in implementing an effective data science pipeline using
Jupyter. You will also see how you can utilize Jupyter's features
to share your documents and codes with your colleagues. The book
also explains how Python 3, R, and Julia can be integrated with
Jupyter for various data science tasks. By the end of this book,
you will comfortably leverage the power of Jupyter to perform
various tasks in data science successfully. Style and approach This
book is a perfect blend of concepts and practical examples, written
in a way that is very easy to understand and implement. It follows
a logical flow where you will be able to build on your
understanding of the different Jupyter features with every chapter.
Learn how to write code, mathematics, graphics, and output, all in
a single document, as well as in a web browser using Project
Jupyter About This Book * Learn to write, execute, and comment your
live code and formulae all under one roof using this unique guide *
This one-stop solution on Project Jupyter will teach you everything
you need to know to perform scientific computation with ease * This
easy-to-follow, highly practical guide lets you forget your worries
in scientific application development by leveraging big data tools
such as Apache Spark, Python, R etc Who This Book Is For This book
caters to all developers, students, or educators who want to
execute code, see output, and comment all in the same document, in
the browser. Data science professionals will also find this book
very useful to perform technical and scientific computing in a
graphical, agile manner. What You Will Learn * Install and run the
Jupyter Notebook system on your machine * Implement programming
languages such as R, Python, Julia, and JavaScript with Jupyter
Notebook * Use interactive widgets to manipulate and visualize data
in real time * Start sharing your Notebook with colleagues * Invite
your colleagues to work with you in the same Notebook * Organize
your Notebook using Jupyter namespaces * Access big data in Jupyter
In Detail Jupyter Notebook is a web-based environment that enables
interactive computing in notebook documents. It allows you to
create and share documents that contain live code, equations,
visualizations, and explanatory text. The Jupyter Notebook system
is extensively used in domains such as data cleaning and
transformation, numerical simulation, statistical modeling, machine
learning, and much more. This book starts with a detailed overview
of the Jupyter Notebook system and its installation in different
environments. Next we'll help you will learn to integrate Jupyter
system with different programming languages such as R, Python,
JavaScript, and Julia and explore the various versions and packages
that are compatible with the Notebook system. Moving ahead, you
master interactive widgets, namespaces, and working with Jupyter in
a multiuser mode. Towards the end, you will use Jupyter with a big
data set and will apply all the functionalities learned throughout
the book. Style and approach This comprehensive practical guide
will teach you how to work with the Jupyter Notebook system. It
demonstrates the integration of various programming languages with
Jupyter Notebook through hands-on examples in every chapter.
If you are a data analyst who has a firm grip on some advanced data
analysis techniques and wants to learn how to leverage the features
of R, this is the book for you. You should have some basic
knowledge of the R language and should know about some data science
topics.
Leverage the power of the popular Jupyter notebooks to simplify
your data science tasks without any hassle Key Features 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 Book DescriptionJupyter 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. 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 Who this book is forThis 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.
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