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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 |
Tabla de Contenidos:
- 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