Jupyter for Data Science

图书信息

作者	Dan Toomey
出版社	Packt Publishing
ISBN	9781785883293
出版时间	2017-10-20
字数	19.5万
分类	进口书,外文原版书,电脑,网络

读书简介

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.

目录

Title Page

Copyright

Jupyter for Data Science

Credits

About the Author

About the Reviewers

www.PacktPub.com

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

Jupyter and Data Science

Jupyter concepts

A first look at the Jupyter user interface

Detailing the Jupyter tabs

What actions can I perform with Jupyter?

What objects can Jupyter manipulate?

Viewing the Jupyter project display

File menu

Edit menu

View menu

Insert menu

Cell menu

Kernel menu

Help menu

Icon toolbar

How does it look when we execute scripts?

Industry data science usage

Real life examples

Finance, Python - European call option valuation

Finance, Python - Monte Carlo pricing

Gambling, R - betting analysis

Insurance, R - non-life insurance pricing

Consumer products, R - marketing effectiveness

Using Docker with Jupyter

Using a public Docker service

Installing Docker on your machine

How to share notebooks with others

Can you email a notebook?

Sharing a notebook on Google Drive

Sharing on GitHub

Store as HTML on a web server

Install Jupyter on a web server

How can you secure a notebook?

Access control

Malicious content

Summary

Working with Analytical Data on Jupyter

Data scraping with a Python notebook

Using heavy-duty data processing functions in Jupyter

Using NumPy functions in Jupyter

Using pandas in Jupyter

Use pandas to read text files in Jupyter

Use pandas to read Excel files in Jupyter

Using pandas to work with data frames

Using the groupby function in a data frame

Manipulating columns in a data frame

Calculating outliers in a data frame

Using SciPy in Jupyter

Using SciPy integration in Jupyter

Using SciPy optimization in Jupyter

Using SciPy interpolation in Jupyter

Using SciPy Fourier Transforms in Jupyter

Using SciPy linear algebra in Jupyter

Expanding on panda data frames in Jupyter

Sorting and filtering data frames in Jupyter/IPython

Filtering a data frame

Sorting a data frame

Summary

Data Visualization and Prediction

Make a prediction using scikit-learn

Make a prediction using R

Interactive visualization

Plotting using Plotly

Creating a human density map

Draw a histogram of social data

Plotting 3D data

Summary

Data Mining and SQL Queries

Special note for Windows installation

Using Spark to analyze data

Another MapReduce example

Using SparkSession and SQL

Combining datasets

Loading JSON into Spark

Using Spark pivot

Summary

R with Jupyter

How to set up R for Jupyter

R data analysis of the 2016 US election demographics

Analyzing 2016 voter registration and voting

Analyzing changes in college admissions

Predicting airplane arrival time

Summary

Data Wrangling

Reading a CSV file

Reading another CSV file

Manipulating data with dplyr

Converting a data frame to a dplyr table

Getting a quick overview of the data value ranges

Sampling a dataset

Filtering rows in a data frame

Adding a column to a data frame

Obtaining a summary on a calculated field

Piping data between functions

Obtaining the 99% quantile

Obtaining a summary on grouped data

Tidying up data with tidyr

Summary

Jupyter Dashboards

Visualizing glyph ready data

Publishing a notebook

Font markdown

List markdown

Heading markdown

Table markdown

Code markdown

More markdown

Creating a Shiny dashboard

R application coding

Publishing your dashboard

Building standalone dashboards

Summary

Statistical Modeling

Converting JSON to CSV

Evaluating Yelp reviews

Summary data

Review spread

Finding the top rated firms

Finding the most rated firms

Finding all ratings for a top rated firm

Determining the correlation between ratings and number of reviews

Building a model of reviews

Using Python to compare ratings

Visualizing average ratings by cuisine

Arbitrary search of ratings

Determining relationships between number of ratings and ratings

Summary

Machine Learning Using Jupyter

Naive Bayes

Naive Bayes using R

Naive Bayes using Python

Nearest neighbor estimator

Nearest neighbor using R

Nearest neighbor using Python

Decision trees

Decision trees in R

Decision trees in Python

Neural networks

Neural networks in R

Random forests

Random forests in R

Summary

Optimizing Jupyter Notebooks

Deploying notebooks

Deploying to JupyterHub

Installing JupyterHub

Accessing a JupyterHub Installation

Jupyter hosting

Optimizing your script

Optimizing your Python scripts

Determining how long a script takes

Using Python regular expressions

Using Python string handling

Minimizing loop operations

Profiling your script

Optimizing your R scripts

Using microbenchmark to profile R script

Modifying provided functionality

Optimizing name lookup

Optimizing data frame value extraction

Changing R Implementation

Changing algorithms

Monitoring Jupyter

Caching your notebook

Securing a notebook

Managing notebook authorization

Securing notebook content

Scaling Jupyter Notebooks

Sharing Jupyter Notebooks

Sharing Jupyter Notebook on a notebook server

Sharing encrypted Jupyter Notebook on a notebook server

Sharing notebook on a web server

Sharing notebook on Docker

Converting a notebook

Versioning a notebook

Summary