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Jupyter Cookbook Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more. [electronic resource]
Toomey, Dan
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| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | Jupyter Cookbook [electronic resource]: Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more./ Dan Toomey. |
| 개인저자 | Toomey, Dan, |
| 발행사항 | Birmingham: Packt Publishing, 2018. |
| 형태사항 | 1 online resource (229 pages). |
| 기타형태 저록 | Print version: Toomey, Dan. Jupyter Cookbook : Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more. Birmingham : Packt Publishing, ©2018 |
| ISBN | 9781788839747 1788839749 |
| 일반주기 |
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| 내용주기 | Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: 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. Chapter 2: Adding an EngineIntroduction; 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 engineHow to do it ... ; Installing the Spark engine; Running a Spark script; Chapter 3: 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 ... ; Chapter 4: 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 RHow 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 ... ; Chapter 5: 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 ... |
| 요약 | 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 applications related to data analysis and visualization. |
| 일반주제명 | Command languages (Computer science) Electronic data processing. Data mining. Information visualization. Python (Computer program language) R (Computer program language) Scala (Computer program language) COMPUTERS / Programming Languages / General. COMPUTERS / Data Processing. Computers --Data Modeling & Design. Database design & theory. Information visualization. Information architecture. Computers --Data Processing. Data capture & analysis. |
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