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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 cu...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Mu 4500 | ||
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| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
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| 019 | |a 1007923378 |a 1079798759 |a 1264758394 | ||
| 020 | |a 9781785883293 |q (electronic bk.) | ||
| 020 | |a 1785883291 |q (electronic bk.) | ||
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| 100 | 1 | |a Toomey, Dan. | |
| 245 | 1 | 0 | |a Jupyter for Data Science. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2017. | ||
| 300 | |a 1 online resource (236 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 500 | |a ""Analyzing 2016 voter registration and voting"" | ||
| 520 | |a 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. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Management information systems. | |
| 650 | 2 | |a Management Information Systems | |
| 650 | 6 | |a Systèmes d'information de gestion. | |
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| 776 | 0 | 8 | |i Print version: |a Toomey, Dan. |t Jupyter for Data Science. |d Birmingham : Packt Publishing, ©2017 |
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