Python for R Users

A Data Science Approach

Ajay Ohri

• Publisher: Wiley
• Year: 2017
• Print ISBN: 9781119126768
• eBook ISBN: 9781119126782
• Edition: 1
• Topic: Informatik
• Subtopic: Programmierung in Python

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python

The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R.

Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing.

• Features a quick-learning format with concise tutorials and actionable analytics

• Provides command-by-command translations of R to Python and vice versa

• Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages

• Offers numerous comparative examples and applications in both programming languages

• Designed for use for practitioners and students that know one language and want to learn the other

• Supplies slides useful for teaching and learning either software on a companion website

Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics.

A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.

1
Introduction to Python R and Data Science

1.1 What Is Python?

Python is a programming language that lets you work more quickly and integrate your systems more effectively. It was created by Guido van Rossum. You can read Guido’s history of Python at the History of Python blog at http://python‐history.blogspot.in/2009/01/introduction‐and‐overview.html.

It is worth reading for beginners and even experienced people in Python. The following is just an extract:

many of Python’s keywords (if, else, while, for, etc.) are the same as in C, Python identifiers have the same naming rules as C, and most of the standard operators have the same meaning as C. Of course, Python is obviously not C and one major area where it differs is that instead of using braces for statement grouping, it uses indentation. For example, instead of writing statements in C like this

if (a < b) {  max = b; } else {  max = a; }

Python just dispenses with the braces altogether (along with the trailing semicolons for good measure) and uses the following structure:

if a < b:  max = b else:  max = a

The other major area where Python differs from C‐like languages is in its use of dynamic typing. In C, variables must always be explicitly declared and given a specific type such as int or double. This information is then used to perform static compile‐time checks of the program as well as for allocating memory locations used for storing the variable’s value. In Python, variables are simply names that refer to objects.

The Python Package Index (PyPI) https://pypi.python.org/pypi hosts third‐party modules for Python. There are currently 91 625 packages there. You can browse Python packages by topic at https://pypi.python.org/pypi?%3Aaction=browse

1.2 What Is R?

The official definition of what is R is given on the main website at http://www.r‐project.org/about.html

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes an effective data handling and storage facility, a suite of operators for calculations on arrays, in particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either on‐screen or on hardcopy, and a well‐developed, simple and effective programming language which includes conditionals, loops, user‐defined recursive functions and input and output facilities.

The term ‘environment’ is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.

The Comprehensive R Archive Network (CRAN) hosts thousands of packages for R at https://cran.r‐project.org/web/packages/, so does GitHub (see https://github.com/search?utf8=%E2%9C%93&q=stars%3A%3E1+language%3AR) as well as Bioconductor as package repositories. You can see all the packages from these repositories for R at http://www.rdocumentation.org/ (11 885 packages as of 2016).

As per the author, R is both a language in statistics as well as computer science and an analytics software with great usefulness in analyzing business data and applying data science to it. In particular the appeal of R remains: it is a free open source and has a huge number of packages particularly dealing with analysis of data.

Disadvantages of R remain memory handling in production environments, lack of incentives for R developers, and a sometimes turgid documentation that is mildly academic oriented rather than enterprise user oriented.

1.3 What Is Data Science?

Data science lies at the intersection of programming, statistics, and business analysis. It is the use of programming tools with...

Table of contents

• Cover
• Title Page
• Table of Contents
• Preface
• Acknowledgments
• Scope
• Purpose
• Plan
• The Zen of Python
• 1 Introduction to Python R and Data Science
• 2 Data Input
• 3 Data Inspection and Data Quality
• 4 Exploratory Data Analysis
• 5 Statistical Modeling
• 6 Data Visualization
• 7 Machine Learning Made Easier
• 8 Conclusion and Summary
• Index
• End User License Agreement