最新章節
- Summary
- Versioning a notebook
- Converting a notebook
- Sharing notebook on Docker
- Sharing notebook on a web server
- Sharing encrypted Jupyter Notebook on a notebook server
品牌：中圖公司
上架時間：2021-07-08 09:16:04
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Summary 更新時間：2021-07-08 09:23:06
- Versioning a notebook
- Converting a notebook
- Sharing notebook on Docker
- Sharing notebook on a web server
- Sharing encrypted Jupyter Notebook on a notebook server
- Sharing Jupyter Notebook on a notebook server
- Sharing Jupyter Notebooks
- Scaling Jupyter Notebooks
- Securing notebook content
- Managing notebook authorization
- Securing a notebook
- Caching your notebook
- Monitoring Jupyter
- Changing algorithms
- Changing R Implementation
- Optimizing data frame value extraction
- Optimizing name lookup
- Modifying provided functionality
- Using microbenchmark to profile R script
- Optimizing your R scripts
- Profiling your script
- Minimizing loop operations
- Using Python string handling
- Using Python regular expressions
- Determining how long a script takes
- Optimizing your Python scripts
- Optimizing your script
- Jupyter hosting
- Accessing a JupyterHub Installation
- Installing JupyterHub
- Deploying to JupyterHub
- Deploying notebooks
- Optimizing Jupyter Notebooks
- Summary
- Random forests in R
- Random forests
- Neural networks in R
- Neural networks
- Decision trees in Python
- Decision trees in R
- Decision trees
- Nearest neighbor using Python
- Nearest neighbor using R
- Nearest neighbor estimator
- Naive Bayes using Python
- Naive Bayes using R
- Naive Bayes
- Machine Learning Using Jupyter
- Summary
- Determining relationships between number of ratings and ratings
- Arbitrary search of ratings
- Visualizing average ratings by cuisine
- Using Python to compare ratings
- Building a model of reviews
- Determining the correlation between ratings and number of reviews
- Finding all ratings for a top rated firm
- Finding the most rated firms
- Finding the top rated firms
- Review spread
- Summary data
- Evaluating Yelp reviews
- Converting JSON to CSV
- Statistical Modeling
- Summary
- Building standalone dashboards
- Publishing your dashboard
- R application coding
- Creating a Shiny dashboard
- More markdown
- Code markdown
- Table markdown
- Heading markdown
- List markdown
- Font markdown
- Publishing a notebook
- Visualizing glyph ready data
- Jupyter Dashboards
- Summary
- Tidying up data with tidyr
- Obtaining a summary on grouped data
- Obtaining the 99% quantile
- Piping data between functions
- Obtaining a summary on a calculated field
- Adding a column to a data frame
- Filtering rows in a data frame
- Sampling a dataset
- Getting a quick overview of the data value ranges
- Converting a data frame to a dplyr table
- Manipulating data with dplyr
- Reading another CSV file
- Reading a CSV file
- Data Wrangling
- Summary
- Predicting airplane arrival time
- Analyzing changes in college admissions
- Analyzing 2016 voter registration and voting
- R data analysis of the 2016 US election demographics
- How to set up R for Jupyter
- R with Jupyter
- Summary
- Using Spark pivot
- Loading JSON into Spark
- Combining datasets
- Using SparkSession and SQL
- Another MapReduce example
- Using Spark to analyze data
- Special note for Windows installation
- Data Mining and SQL Queries
- Summary
- Plotting 3D data
- Draw a histogram of social data
- Creating a human density map
- Plotting using Plotly
- Interactive visualization
- Make a prediction using R
- Make a prediction using scikit-learn
- Data Visualization and Prediction
- Summary
- Sorting a data frame
- Filtering a data frame
- Sorting and filtering data frames in Jupyter/IPython
- Expanding on panda data frames in Jupyter
- Using SciPy linear algebra in Jupyter
- Using SciPy Fourier Transforms in Jupyter
- Using SciPy interpolation in Jupyter
- Using SciPy optimization in Jupyter
- Using SciPy integration in Jupyter
- Using SciPy in Jupyter
- Calculating outliers in a data frame
- Manipulating columns in a data frame
- Using the groupby function in a data frame
- Using pandas to work with data frames
- Use pandas to read Excel files in Jupyter
- Use pandas to read text files in Jupyter
- Using pandas in Jupyter
- Using NumPy functions in Jupyter
- Using heavy-duty data processing functions in Jupyter
- Data scraping with a Python notebook
- Working with Analytical Data on Jupyter
- Summary
- Malicious content
- Access control
- How can you secure a notebook?
- Install Jupyter on a web server
- Store as HTML on a web server
- Sharing on GitHub
- Sharing a notebook on Google Drive
- Can you email a notebook?
- How to share notebooks with others
- Installing Docker on your machine
- Using a public Docker service
- Using Docker with Jupyter
- Consumer products R - marketing effectiveness
- Insurance R - non-life insurance pricing
- Gambling R - betting analysis
- Finance Python - Monte Carlo pricing
- Finance Python - European call option valuation
- Real life examples
- Industry data science usage
- How does it look when we execute scripts?
- Icon toolbar
- Help menu
- Kernel menu
- Cell menu
- Insert menu
- View menu
- Edit menu
- File menu
- Viewing the Jupyter project display
- What objects can Jupyter manipulate?
- What actions can I perform with Jupyter?
- Detailing the Jupyter tabs
- A first look at the Jupyter user interface
- Jupyter concepts
- Jupyter and Data Science
- Questions
- Piracy
- Errata
- Downloading the example code
- Customer support
- Reader feedback
- Conventions
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- Customer Feedback
- Why subscribe?
- www.PacktPub.com
- About the Reviewers
- About the Author
- Credits
- Jupyter for Data Science
- Copyright
- Title Page
- coverpage
- coverpage
- 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 更新時間：2021-07-08 09:23:06
