With its variety of coding styles R allows you to access a particular value in a data frame in several manners. If your coding is accessing a large data frame frequently to extract singular values it is worthwhile to benchmark different alternatives to see which access method provides the most performance.
- Preface
- 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
- How does it look when we execute scripts?
- Industry data science usage
- Real life examples
- Using Docker with Jupyter
- How to share notebooks with others
- How can you secure a notebook?
- Summary
- Working with Analytical Data on Jupyter
- Data Visualization and Prediction
- Data Mining and SQL Queries
- R with Jupyter
- Data Wrangling
- Jupyter Dashboards
- Statistical Modeling
- Machine Learning Using Jupyter
- Optimizing Jupyter Notebooks
Optimizing data frame value extraction
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