An edition of Jupyter for Data Science (2017)

Jupyter for Data Science

Jupyter for Data Science
Dan Toomey, Dan Toomey
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Last edited by MARC Bot
December 17, 2022 | History
An edition of Jupyter for Data Science (2017)

Jupyter for Data Science

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 -- Chapter 9: 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 -- Chapter 10: 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 -- Index.

Publish Date
Publisher
Packt Publishing
Language
English
Pages
236

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Edition Availability
Cover of: Jupyter for Data Science
Jupyter for Data Science
2017, Packt Publishing
in English

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Book Details


Table of Contents

Cover
Copyright
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Table of Contents
Preface
Chapter 1: 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 serverInstall Jupyter on a web server
How can you secure a notebook?
Access control
Malicious content
Summary
Chapter 2: 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 frameCalculating 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
Chapter 3: Data Visualization and Prediction
Make a prediction using scikit-learn
Make a prediction using RInteractive visualization
Plotting using Plotly
Creating a human density map
Draw a histogram of social data
Plotting 3D data
Summary
Chapter 4: 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
Chapter 5: R with Jupyter
How to set up R for Jupyter
R data analysis of the 2016 US election demographics

Edition Notes

""Analyzing 2016 voter registration and voting""

Published in
Birmingham

Classifications

Dewey Decimal Class
005.1
Library of Congress
T58.6, T55.4-60.8

The Physical Object

Pagination
1 online resource (236 pages)
Number of pages
236

ID Numbers

Open Library
OL44313313M
ISBN 10
1785883291
ISBN 13
9781785883293
OCLC/WorldCat
1009240867
Wikidata
Q106987817

Source records

marc_columbia MARC record

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