Python and R for the Modern Data Scientist: The Best of Both Worlds
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. With this book, data scientists from the Python and R communities will learn how to speak the dialects of each language. By recognizing the strengths of working with both, you’ll discover new ways to accomplish data science tasks and expand your skill set.
Authors Boyan Angelov and Rick Scavetta explain the parallel structures of these languages and highlight where each one excels, whether it’s their linguistic features or the powers of their open source ecosystems. Not only will you learn how to use Python and R together in real-world settings, but you’ll also broaden your knowledge and job opportunities by working as a bilingual data scientist.
- Learn Python and R from the perspective of your current language
- Understand the strengths and weaknesses of each language
- Identify use cases where one language is better suited than the other
- Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows
- Learn how to integrate R and Python in a single workflow
- Follow a real-world case study that demonstrates ways to use these languages together
Table of contents
I. Discovery of a New Language
1. In the Beginning
II. Bilingualism I: Learning a New Language
2. R for Pythonistas
3. Python for UseRs
III. Bilingualism II: The Modern Context
4. Data Format Context
5. Workflow Context
IV. Bilingualism III: Becoming Synergistic
6. Using the Two Languages Synergistically
7. A Case Study in Bilingual Data Science
A. A Python:R Bilingual Dictionary
Preface Why We Wrote This Book Technical Interactions Who This Book Is For Prerequisites How This Book Is Organized Let’s Talk Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments I. Discovery of a New Language 1. In the Beginning The Origins of R The Origins of Python The Language War Begins The Battle for Data Science Dominance A Convergence on Cooperation and Community-Building Final Thoughts II. Bilingualism I: Learning a New Language 2. R for Pythonistas Up and Running with R Projects and Packages The Triumph of Tibbles A Word About Types and Exploring Naming (Internal) Things Lists The Facts About Factors How to Find…Stuff Reiterations Redo Final Thoughts 3. Python for UseRs Versions and Builds Standard Tooling Virtual Environments Installing Packages Notebooks How Does Python, the Language, Compare to R? Import a Dataset Examine the Data Data Structures and Descriptive Statistics Data Structures: Back to the Basics Indexing and Logical Expressions Plotting Inferential Statistics Final Thoughts III. Bilingualism II: The Modern Context 4. Data Format Context External Versus Base Packages Image Data Text Data Time Series Data Base R Prophet Spatial Data Final Thoughts 5. Workflow Context Defining Workflows Exploratory Data Analysis Static Visualizations Interactive Visualizations Machine Learning Data Engineering Reporting Static Reporting Interactive Reporting Final Thoughts IV. Bilingualism III: Becoming Synergistic 6. Using the Two Languages Synergistically Faux Operability Interoperability Going Deeper Pass Objects Between R and Python in an R Markdown Document Call Python in an R Markdown Document Call Python by Sourcing a Python Script Call Python Using the REPL Call Python with Dynamic Input in an Interactive Document Final Thoughts 7. A Case Study in Bilingual Data Science 24 Years and 1.88 Million Wildfires Setup and Importing Data EDA and Data Visualization Machine Learning Setting Up Our Python Environment Feature Engineering Model Training Prediction and UI Final Thoughts A. A Python:R Bilingual Dictionary Package Management Assign Operators Types Arithmetic Operators Attributes Keywords Functions and Methods Style and Naming Conventions Analogous Data Storage Objects Data Frames Logical Expressions Indexing Index
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